phd programs computational biology

Graduate Programs

Computational biology.

The Center for Computational Molecular Biology (CCMB) offers Ph.D. degrees in Computational Biology to train the next generation of scientists to perform cutting edge research in the multidisciplinary field of Computational Biology.

During the course of their Ph.D. studies students will develop and apply novel computational, mathematical , and statistical techniques to problems in the life sciences. Students in this program must achieve mastery in three areas - computational science, molecular biology, and probability and statistical inference - through a common core of studies that spans and integrates these areas.

The Ph.D. program in Computational Biology draws on course offerings from the disciplines of the Center’s Core faculty members. These areas are Applied Mathematics, Computer Science, the Division of Biology and Medicine, the Center for Biomedical Informatics, and the School of Public Health. Our faculty and Director of Graduate Studies work with each student to develop the best plan of coursework and research rotations to meet the student’s goals in their research focus and satisfy the University’s requirements for graduation.

Applicants should state a preference for at least one of these areas in their personal statement or elsewhere in their application. In addition, students interested in the intersection of Applied Mathematics and Computational Biology are encouraged to apply directly to the  Applied Mathematics Ph.D. program , and also to contact relevant  CCMB faculty members .

Our Ph.D. program assumes the following prerequisites: mathematics through intermediate calculus, linear algebra and discrete mathematics, demonstrated programming skill, and at least one undergraduate course in chemistry and in molecular biology. Exceptional strengths in one area may compensate for limited background in other areas, but some proficiency across the disciplines must be evident for admission.

Additional Resources

CCMB computing resources include a set of multiprocessor computer clusters and data storage servers with 392 processors. The CCMB Cluster is the largest dedicated computing system on campus for computational biology and bioinformatics applications. See also answers to  frequently asked questions .

Application Information

Application requirements, gre subject:.

Not required

GRE General:

Personal statement:.

Applicants will be asked a series of short form questions regarding their interest in computational biology, their research experiences, and their goals for the future. 1) Describe the life experiences that inspired you to pursue a career in science. 2) Describe at least one research experience you have had that prepared you intellectually/ scientifically for a career in computational biology. 3) Explain at least one challenge you have overcome in life or research to pursue a scientific career and what you have learned from this experience. 4) Discuss any broader impacts that you have had on your community (e.g. family, educational institution, or broader community). 5) Why would you like to pursue your PhD in the Brown CCMB program? (Include at least two faculty members who you would like to work with at Brown and why.)

Dates/Deadlines

Application deadline, completion requirements.

Six graduate–level courses, two eight–week laboratory rotations, preliminary research presentation, dissertation, oral defense

Contact and Location

Center for computational molecular biology, location address, mailing address.

• Program Faculty
• Program Handbook
• Graduate School Handbook

Computational Biology Graduate Programs in America

1-18 of 18 results

MIT School of Science

Cambridge, MA •

Massachusetts Institute of Technology •

Graduate School

Massachusetts Institute of Technology ,

Graduate School ,

CAMBRIDGE, MA ,

Harvard John A. Paulson School of Engineering and Applied Sciences

Harvard University •

Harvard University ,

Princeton University

Princeton, NJ •

• • Rating 4.33 out of 5   3 reviews

Master's Student: The best part of the Princeton University mechanical engineering graduate degree is the excellent faculty that teach the courses. They are incredibly knowledgeable and also very willing to help students in office hours or in sponsorship of projects. The worst part of the Princeton University mechanical engineering graduate degree is the lack of structure for the graduate research program which can leave you feeling unsure on the direction of your research. ... Read 3 reviews

PRINCETON, NJ ,

3 Niche users give it an average review of 4.3 stars.

Featured Review: Master's Student says The best part of the Princeton University mechanical engineering graduate degree is the excellent faculty that teach the courses. They are incredibly knowledgeable and also very willing to help... .

Read 3 reviews.

Kenneth P. Dietrich School of Arts and Sciences

University of Pittsburgh •

Graduate School •

PITTSBURGH, PA

University of Pittsburgh

• • Rating 4.43 out of 5   74

Mississippi State University

MISSISSIPPI STATE, MS

• • Rating 4.53 out of 5   51

Brown University Graduate School

Providence, RI •

Brown University •

Brown University ,

PROVIDENCE, RI ,

Dornsife College of Letters, Arts and Sciences

Los Angeles, CA •

University of Southern California •

University of Southern California ,

LOS ANGELES, CA ,

Cornell University College of Agriculture and Life Sciences

Ithaca, NY •

Cornell University •

Cornell University ,

ITHACA, NY ,

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Weill Cornell Graduate School of Medical Sciences

New York, NY •

• • Rating 4.75 out of 5   4 reviews

Doctoral Student: The coursework was relevant, a little disjointed but good. There could be a wider range of courses that would be great, but it’s nice that we can take courses from other universities. ... Read 4 reviews

NEW YORK, NY ,

4 Niche users give it an average review of 4.8 stars.

Featured Review: Doctoral Student says The coursework was relevant, a little disjointed but good. There could be a wider range of courses that would be great, but it’s nice that we can take courses from other universities. .

Read 4 reviews.

Mellon College of Science

Pittsburgh, PA •

Carnegie Mellon University •

Blue checkmark.

Carnegie Mellon University ,

PITTSBURGH, PA ,

UC Berkeley College of Letters & Science

Berkeley, CA •

University of California - Berkeley •

University of California - Berkeley ,

BERKELEY, CA ,

Case Western Reserve University School of Dental Medicine

Cleveland, OH •

Case Western Reserve University •

Case Western Reserve University ,

CLEVELAND, OH ,

University of Pittsburgh ,

College of Liberal Arts & Sciences - The University of Kansas

Lawrence, KS •

The University of Kansas •

The University of Kansas ,

LAWRENCE, KS ,

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CU Anschutz Medical Campus Graduate School

Aurora, CO •

University of Colorado Denver •

• • Rating 4.39 out of 5   31 reviews

Doctoral Student: Cu Denver is a great school to go to. I am passionate efgyaheurailagerkugvkabehianfvurisgb hfzisvbeiuhrvbiauelrbhfuawheuif,awjvbahauyewabvfiewbagfiyabehjfbaweyugwuyeagfayukbaueygaiehrilaeghviaehrgvuarageuaihvgaiulefialbfgaygfeuyrhoiafaueirhgoiaerguiaehiurghauerhgaeqzfhvbfvbilhhbvzfhjbvlifvbisfvislbvisfivlsuvliseulthshgbilsrghruls ... Read 31 reviews

University of Colorado Denver ,

AURORA, CO ,

31 Niche users give it an average review of 4.4 stars.

Featured Review: Doctoral Student says Cu Denver is a great school to go to. I am passionate efgyaheurailagerkugvkabehianfvurisgb... .

Read 31 reviews.

University of Texas - Arlington College of Science

Arlington, TX •

University of Texas - Arlington •

University of Texas - Arlington ,

ARLINGTON, TX ,

The Graduate School - Rutgers University - Camden

Camden, NJ •

Rutgers University–Camden •

• • Rating 5 out of 5   4 reviews

Master's Student: Rigorous and challenging but definitely the push I needed to gain opportunities in the professional field of public service. The academics at Rutgers-Camden fosters an eager environment that promotes diversity, group support, and the understanding of development in a well-rounded setting. The professional field of public administration is forever evolving and I feel confident that my education has prepared me to be a resilient, open minded, and capacity building leader to help make this world a better fit for all. I have been able to gain more knowledge of the principles of humanitarian aid as well as local and national policies. ... Read 4 reviews

Rutgers University–Camden ,

CAMDEN, NJ ,

4 Niche users give it an average review of 5 stars.

Featured Review: Master's Student says Rigorous and challenging but definitely the push I needed to gain opportunities in the professional field of public service. The academics at Rutgers-Camden fosters an eager environment that promotes... .

McGovern Medical School

Houston, TX •

University of Texas - Health Science Center at Houston •

• • Rating 5 out of 5   3 reviews

Niche User: The cardiac perfusion program is only 1 year long! The program does not require you to live on campus ! It’s not very expensive either ! ... Read 3 reviews

University of Texas - Health Science Center at Houston ,

HOUSTON, TX ,

3 Niche users give it an average review of 5 stars.

Featured Review: Niche User says The cardiac perfusion program is only 1 year long! The program does not require you to live on campus ! It’s not very expensive either ! .

Weill Cornell Medical College

• • Rating 5 out of 5   2 reviews

Doctoral Student: It is an amazing school with a vast number of resources. However, the school puts a heavy financial burden on international students by requiring them to pay for all four years of tuition upfront with any financial assistance. ... Read 2 reviews

2 Niche users give it an average review of 5 stars.

Featured Review: Doctoral Student says It is an amazing school with a vast number of resources. However, the school puts a heavy financial burden on international students by requiring them to pay for all four years of tuition upfront... .

Read 2 reviews.

Graduate School of Biomedical Sciences - Baylor College of Medicine

Baylor College of Medicine •

Baylor College of Medicine ,

Showing results 1 through 18 of 18

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Center for Computational Biology

Computational Biology PhD

The main objective of the Computational Biology PhD is to train the next generation of scientists who are both passionate about exploring the interface of computation and biology, and committed to functioning at a high level in both computational and biological fields.

The program emphasizes multidisciplinary competency, interdisciplinary collaboration, and transdisciplinary research, and offers an integrated and customizable curriculum that consists of two semesters of didactic course work tailored to each student’s background and interests, research rotations with faculty mentors spanning computational biology’s core disciplines, and dissertation research jointly supervised by computational and biological faculty mentors.

The Computational Biology Graduate Group facilitates student immersion into UC Berkeley’s vibrant computational biology research community. Currently, the Group includes over 46 faculty from across 14 departments of the College of Letters and Science, the College of Engineering, the College of Natural Resources, and the School of Public Health. Many of these faculty are available as potential dissertation research advisors for Computational Biology PhD students, with more available for participation on doctoral committees.

The First Year

The time to degree (normative time) of the Computational Biology PhD is five years. The first year of the program emphasizes gaining competency in computational biology, the biological sciences, and the computational sciences (broadly construed). Since student backgrounds will vary widely, each student will work with faculty and student advisory committees to develop a program of study tailored to their background and interests. Specifically, all first-year students must:

• Perform three rotations with Core faculty (one rotation with a non-Core faculty is acceptable with advance approval)
• Complete course work requirements (see below)
• Complete a course in the Responsible Conduct of Research
• Attend the computational biology seminar series
• Complete experimental training (see below)

Laboratory Rotations

Entering students are required to complete three laboratory rotations during their first year in the program to seek out a Dissertation Advisor under whose supervision dissertation research will be conducted. Students should rotate with at least one computational Core faculty member and one experimental Core faculty member. Click here to view rotation policy. 

Course Work & Additional Requirements

Students must complete the following coursework in the first three (up to four) semesters. Courses must be taken for a grade and a grade of B or higher is required for a course to count towards degree progress:

• Fall and Spring semester of CMPBIO 293, Doctoral Seminar in Computational Biology
• A Responsible Conduct of Research course, most likely through the Department of Molecular and Cell Biology.
• STAT 201A & STAT 201B : Intro to Probability and Statistics at an Advanced Level. Note: Students who are offered admission and are not prepared to complete STAT 201A and 201B will be required to complete STAT 134 or PH 142 first.
• CS61A : The Structure and Interpretation of Computer Programs. Note: students with the equivalent background can replace this requirement with a more advanced CS course of their choosing.
• 3 elective courses relevant to the field of Computational Biology , one of which must be at the graduate level (see below for details).
• Attend the computational biology invited speaker seminar series. A schedule is circulated to all students by email and is available on the Center website. Starting with the 2023 entering class, CCB PhD students must enroll in CMPBIO 275: Computational Biology Seminar , which provides credit for this seminar series.
• 1) completion of a laboratory course at Berkeley with a minimum grade of B,
• 2) completion of a rotation in an experimental lab (w/ an experimental project), with a positive evaluation from the PI,
• a biological sciences undergraduate major with at least two upper division laboratory-based courses,
• a semester or equivalent of supervised undergraduate experimental laboratory-based research at a university,
• or previous paid or volunteer/internship work in an industry-based experimental laboratory.

Students are expected to develop a course plan for their program requirements and to consult with the Head Graduate Advisor before the Spring semester of their first year for formal approval (signature required). The course plan will take into account the student’s undergraduate training areas and goals for PhD research areas.

Satisfactory completion of first year requirements will be evaluated at the end of the spring semester of the first year. If requirements are satisfied, students will formally choose a Dissertation advisor from among the core faculty with whom they rotated and begin dissertation research.

Waivers: Students may request waivers for the specific courses STAT 201A, STAT 201B, and CS61A. In all cases of waivers, the student must take alternative courses in related areas so as to have six additional courses, as described above. For waiving out of STAT 201A/B, students can demonstrate they have completed the equivalent by passing a proctored assessment exam on Campus. For waiving out CS61A, the Head Graduate Advisor will evaluate student’s previous coursework based on the previous course’s syllabus and other course materials to determine equivalency.

Electives: Of the three electives, students are required to choose one course in each of the two following cluster areas:

• Cluster A (Biological Science) : These courses are defined as those for which the learning goals are primarily related to biology. This includes courses covering topics in molecular biology, genetics, evolution, environmental science, experimental methods, and human health. This category may also cover courses whose focus is on learning how to use bioinformatic tools to understand experimental data.
• Cluster B (Computational Sciences): These courses are defined as those for which the learning goals involve computing, inference, or mathematical modeling, broadly defined. This includes courses on algorithms, computing languages or structures, mathematical or probabilistic concepts, and statistics. This category would include courses whose focus is on biological applications of such topics.

In the below link we give some relevant such courses, but students can take courses beyond this list; for courses not on this list, the Head Graduate Advisor will determine to which cluster a course can be credited. For classes that have significant overlap between these two clusters, the department which offers the course may influence the decision of the HGA as to whether the course should be assigned to cluster A or B.

See below for some suggested courses in these categories:

Suggested Coursework Options

Second Year & Beyond

At the beginning of the fall of the second year, students begin full-time dissertation research in earnest under the supervision of their Dissertation advisor. It is anticipated that it will take students three (up to four) semesters to complete the 6 course requirement. Students are required to continue to participate annually in the computational biology seminar series.

Qualifying Examination

Students are expected to take and pass an oral Qualifying Examination (QE) by the end of the spring semester (June 15th) of their second year of graduate study. Students must present a written dissertation proposal to the QE committee no fewer than four weeks prior to the oral QE. The write-up should follow the format of an NIH-style grant proposal (i.e., it should include an abstract, background and significance, specific aims to be addressed (~3), and a research plan for addressing the aims) and must thoroughly discuss plans for research to be conducted in the dissertation lab. Click here for more details on the guidelines and format for the QE. Click here to view the rules for the composition of the committee and the form for declaring your committee.

Advancement to Candidacy

After successfully completing the QE, students will Advance to Candidacy. At this time, students select the members of their dissertation committee and submit this committee for approval to the Graduate Division. Students should endeavor to include a member whose research represents a complementary yet distinct area from that of the dissertation advisor (ie, biological vs computational, experimental vs theoretical) and that will be integrated in the student’s dissertation research. Click here to view the rules for the composition of the committee and the form for declaring your committee.

Meetings with the Dissertation Committee

After Advancing to Candidacy, students are expected to meet with their Dissertation Committee at least once each year.

Teaching Requirements

Computational Biology PhD students are required to teach at least two semesters (starting with Fall 2019 class), but may teach more. The requirement can be modified if the student has funding that does not allow teaching. Starting with the Fall 2019 class: At least one of those courses should require that you teach a section. Berkeley Connect or CMPBIO 293 can count towards one of the required semesters.

The Dissertation

Dissertation projects will represent scholarly, independent and novel research that contributes new knowledge to Computational Biology by integrating knowledge and methodologies from both the biological and computational sciences. Students must submit their dissertation by the May Graduate Division filing deadline (see Graduate Division for date) of their fifth–and final–year.

Special Requirements

Students will be required to present their research either orally or via a poster at the annual retreat beginning in their second year.

• Financial Support

The Computational Biology Graduate Group provides a competitive stipend (the stipend for 2023-24 is $43,363) as well as full payment of fees and non-resident tuition (which includes health care). Students maintaining satisfactory academic progress are provided full funding for five to five and a half years. The program supports students in the first year, while the PI/mentor provides support from the second year on. A portion of this support is in the form of salary from teaching assistance as a Graduate Student Instructor (GSI) in allied departments, such as Molecular and Cell Biology, Integrative Biology, Plant and Microbial Biology, Mathematics, Statistics or Computer Science. Teaching is part of the training of the program and most students will not teach more than two semesters, unless by choice.

Due to cost constraints, the program admits few international students; the average is two per year. Those admitted are also given full financial support (as noted above): stipend, fees and tuition.

Students are also strongly encouraged to apply for extramural fellowships for the proposal writing experience. There are a number of extramural fellowships that Berkeley students apply for that current applicants may find appealing. Please note that the NSF now only allows two submissions – once as an undergrad and once in grad school. The NSF funds students with potential, as opposed to specific research projects, so do not be concerned that you don’t know your grad school plans yet – just put together a good proposal! Although we make admissions offers before the fellowships results are released, all eligible students should take advantage of both opportunities to apply, as it’s a great opportunity and a great addition to a CV.

• National Science Foundation Graduate Research Fellowship (app deadlines in Oct)
• Hertz Foundation Fellowship (app deadline Oct)
• National Defense Science and Engineering Graduate Fellowship (app deadline in mid-Fall)
• DOE Computational Science Graduate Fellowship (Krell Institute) (app deadline in Jan)

CCB no longer requires the GRE for admission (neither general, nor subject). The GRE will not be seen by the review committee, even if sent to Berkeley.

PLEASE NOTE: The application deadline is Wednesday, November 30 , 2023, 8:59 PST/11:59 EST

If you would like to learn more about our program, you can watch informational YouTube videos from the past two UC Berkeley Graduate Diversity Admissions Fairs: 2021 recording & 2020 recording .

We invite applications from students with distinguished academic records, strong foundations in the basic biological, physical and computational sciences, as well as significant computer programming and research experience. Admission for the Computational Biology PhD is for the fall semester only, and Computational Biology does not offer a Master’s degree.

We are happy to answer any questions you may have, but please be sure to read this entire page first, as many of your questions will be answered below or on the Tips tab.

IMPORTANT : Please note that it is not possible to select a specific PhD advisor until the end of the first year in the program, so contacting individual faculty about openings in their laboratories will not increase your chances of being accepted into the program. You will have an opportunity to discuss your interests with relevant faculty if you are invited to interview in February.

Undergraduate Preparation

Minimum requirements for admission to graduate study:

• A bachelor’s degree or recognized equivalent from an accredited institution.
• Minimum GPA of 3.0.
• Undergraduate preparation reflecting a balance of training in computational biology’s core disciplines (biology, computer science, statistics/mathematics), for example, a single interdisciplinary major, such as computational biology or bioinformatics; a major in a core discipline and a combination of interdisciplinary course work and research experiences; or a double major in core disciplines.
• Basic research experience and aptitude are key considerations for admission, so evidence of research experience and letters of recommendation from faculty mentors attesting to the applicant’s research experience are of particular interest.
• GRE – NOT required or used for review .
• TOEFL scores for international students (see below for details).

Application Requirements

ALL materials, including letters, are due November 30, 2023 (8:59 PST). More information is provided and required as part of the online application, so please create an account and review the application before emailing with questions (and please set up an account well before the deadline):

• A completed graduate application: The online application opens in early or mid-September and is located on the Graduate Division website . Paper applications are not accepted. Please create your account and review the application well ahead of the submit date , as it will take time to complete and requests information not listed here.
• A nonrefundable application fee: The fee must be paid using a major credit card and is not refundable. For US citizens and permanent residents, the fee is $135; US citizens and permanent residents may request a fee waiver as part of the online application. For all other students (international) the fee is $155 (no waivers, no exceptions). Graduate Admissions manages the fee, not the program, so please contact them with questions.
• Three letters of recommendation, minimum (up to five are accepted): Letters of recommendation must be submitted online as part of the Graduate Division’s application process. Letters are also due November 30, so please inform your recommenders of this deadline and give them sufficient advance notice. It is your responsibility to monitor the status of your letters of recommendation (sending prompts, as necessary) in the online system.
• Transcripts: Unofficial copies of all relevant transcripts, uploaded as part of the online application (see application for details). Scanned copies of official transcripts are strongly preferred, as transcripts must include applicant and institution name and degree goal and should be easy for the reviewers to read (print-outs from online personal schedules can be hard to read and transcripts without your name and the institution name cannot be used for review). Do not send via mail official transcripts to Grad Division or Computational Biology, they will be discarded.
• Essays: Follow links to view descriptions of what these essays should include ( Statement of Purpose [2-3 pages], Personal Statement [1-2 pages]). Also review Tips tab for formatting advice.
• (Highly recommended) Applicants should consider applying for extramural funding, such as NSF Fellowships. These are amazing opportunities and the application processes are great preparation for graduate studies. Please see Financial Support tab.
• Read and follow all of the “Application Tips” listed on the last tab. This ensures that everything goes smoothly and you make a good impression on the faculty reviewing your file.

The GRE general test is not required. GRE subject tests are not required. GRE scores will not be a determining factor for application review and admission, and will NOT be seen by the CCB admissions committee. While we do not encourage anyone to take the exam, in case you decide to apply to a different program at Berkeley that does require them: the UC Berkeley school code is 4833; department codes are unnecessary. As long as the scores are sent to UC Berkeley, they will be received by any program you apply to on campus.

TOEFL/IELTS

Adequate proficiency in English must be demonstrated by those applicants applying from countries where English is not the official language. There are two standardized tests you may take: the Test of English as a Foreign Language (TOEFL), and the International English Language Testing System (IELTS). TOEFL minimum passing scores are 90 for the  Internet-based test (IBT) , and 570 for the paper-based format (PBT) . The TOEFL may be waived if an international student has completed at least one year of full-time academic course work with grades of B or better while in residence at a U.S. university (transcript will be required). Please click here for more information .

Application Deadlines

The Application Deadline is 8:59 pm Pacific Standard Time, November 30, 2023 . The application will lock at 9pm PST, precisely. All materials must be received by the deadline. While rec letters can continue to be submitted and received after the deadline, the committee meets in early December and will review incomplete applications. TOEFL tests should be taken by or before the deadline, but self-reported scores are acceptable for review while the official scores are being processed. All submitted applications will be reviewed, even if materials are missing, but it may impact the evaluation of the application.

It is your responsibility to ensure and verify that your application materials are submitted in a timely manner. Please be sure to hit the submit button when you have completed the application and to monitor the status of your letters of recommendation (sending prompts, as necessary). Please include the statement of purpose and personal statement in the online application. While you can upload a CV, please DO NOT upload entire publications or papers. Please DO NOT send paper résumés, separate folders of information, or articles via mail. They will be discarded unread.

The Computational Biology Interview Visit dates will be: February 25-27, 2024

Top applicants who are being considered for admission will be invited to visit campus for interviews with faculty. Invitations will be made by early January. Students are expected to stay for the entire event, arriving in Berkeley by 5:30pm on the first day and leaving the evening of the final day. In the application, you must provide the names of between 7-10 faculty from the Computational Biology website with whom you are interested in conducting research or performing rotations. This helps route your application to our reviewers and facilitates the interview scheduling process. An invitation is not a guarantee of admission.

International students may be interviewed virtually, as flights are often prohibitively expensive.

Tips for the Application Process

Uploaded Documents: Be sure to put your name and type of essay on your essays ( Statement of Purpose [2-3 pages], Personal Statement [1-2 pages]) as a header or before the text, whether you use the text box or upload a PDF or Word doc. There is no minimum length on either essay, but 3 pages maximum is suggested. The Statement of Purpose should describe your research and educational background and aspirations. The Personal Statement can include personal achievements not necessarily related to research, barriers you’ve had to overcome, mentoring and volunteering activities, things that make you unique and demonstrate the qualities you will bring to the program.

Letters of Recommendation: should be from persons who have supervised your research or academic work and who can evaluate your intellectual ability, creativity, leadership potential and promise for productive scholarship. If lab supervision was provided by a postdoc or graduate student, the letter should carry the signature or support of the faculty member in charge of the research project. Note: the application can be submitted before all of the recommenders have completed their letters. It is your responsibility to keep track of your recommender’s progress through the online system. Be sure to send reminders if your recommenders do not submit their letters.

Extramural fellowships: it is to your benefit to apply for fellowships as they may facilitate entry into the lab of your choice, are a great addition to your CV and often provide higher stipends. Do not allow concerns about coming up with a research proposal before joining a lab prevent you from applying. The fellowships are looking for research potential and proposal writing skills and will not hold you to specific research projects once you have started graduate school.

Calculating GPA: Schools can differ in how they assign grades and calculate grade point averages, so it may be difficult for this office to offer advice. The best resource for calculating the GPA for your school is to check the back of the official transcripts where a guide is often provided or use an online tool. There are free online GPA conversion tools that can be found via an internet search.

Faculty Contact/Interests: Please be sure to list faculty that interest you as part of the online application. You are not required to contact any faculty in advance, nor will it assist with admission, but are welcome to if you wish to learn more about their research.

Submitting the application: To avoid the possibility of computer problems on either side, it is NOT advisable to wait until the last day to start and/or submit your application. It is not unusual for the application system to have difficulties during times of heavy traffic. However, there is no need to submit the application too early. No application will be reviewed before the deadline.

Visits: We only arrange one campus visit for recruitment purposes. If you are interested in visiting the campus and meeting with faculty before the application deadline, you are welcome to do so on your own time (we will be unable to assist).

Name: Please double check that you have entered your first and last names in the correct fields. This is our first impression of you as a candidate, so you do want to get your name correct! Be sure to put your name on any documents that you upload (Statement of Purpose, Personal Statement).

California Residency: You are not considered a resident if you hope to enter our program in the Fall, but have never lived in California before or are here on a visa. So, please do not mark “resident” on the application in anticipation of admission. You must have lived in California previously, and be a US citizen or Permanent Resident, to be a resident.

Faculty Leadership Head Graduate Advisor and Chair for the PhD & DE John Huelsenbeck ( [email protected] )

Associate Head Graduate Advisor for PhD & DE Liana Lareau ( [email protected] )

Equity Advisor Rasmus Nielsen ( [email protected] )

Director of CCB Elizabeth Purdom ( [email protected] )

Core PhD & DE Faculty ( link )

Staff support Student Services Advisor (GSAO): Kate Chase ( [email protected] )

Ph.D. in Computational Biology and Bioinformatics

General info.

• Faculty working with students: 60
• Students: 29
• Part time study available: No
• Application Terms: Fall
• Application Deadline: November 30

Monica Franklin Program Coordinator CBB Graduate Program Duke University Box 90090 Durham, NC 27708

Phone: 919-668-1049

Email: [email protected]

Website:  https://medschool.duke.edu/education/biomedical-phd-programs/computational-biology-and-bioinformatics-program

Program Description

The mission of the Graduate Program in Computational Biology and Bioinformatics (CBB) is to train predoctoral students to become leaders at the interdisciplinary intersection of quantitative and biomedical sciences. The program provides rigorous training in quantitative approaches from computer science, statistics, mathematics, physics, and engineering that enable its students to successfully address contemporary challenges across biology and medicine.  CBB trains students who have an interest and aptitude in both the computational and biological sciences. During their time in the program, students develop expertise in one or more quantitative areas, as well as in the specific biological area on which their research focuses.

Certificate in CBB

For students enrolled in other Ph.D. or masters programs of participating departments, the program also offers the opportunity to pursue a certificate in CBB. Students qualify for a CBB certificate by successfully completing two core courses plus an additional CBB course. Registration for the Computational Biology seminar every semester except the semester of graduation is also required.

• Computational Biology and Bioinformatics: PhD Admissions and Enrollment Statistics
• Computational Biology and Bioinformatics: PhD Completion Rate Statistics
• Computational Biology and Bioinformatics: PhD Time to Degree Statistics
• Computational Biology and Bioinformatics: PhD Career Outcomes Statistics

Application Information

Application Terms Available:  Fall

Application Deadline:  November 30

Graduate School Application Requirements See the Application Instructions page for important details about each Graduate School requirement.

• Transcripts: Unofficial transcripts required with application submission; official transcripts required upon admission
• Letters of Recommendation: 3 Required
• Statement of Purpose: Required
• Résumé: Required
• GRE Scores: GRE General (Optional)
• English Language Exam: TOEFL, IELTS, or Duolingo English Test required* for applicants whose first language is not English *test waiver may apply for some applicants
• GPA: Undergraduate GPA calculated on 4.0 scale required

Department-Specific Application Requirements (submitted through online application)

Writing Sample None required

Additional Components Optional Video Essay: How would a Duke PhD training experience help you achieve your academic and professional goals? Max video length 2 minutes; record externally and provide URL in application.

We strongly encourage you to review additional department-specific application guidance from the program to which you are applying: Departmental Application Guidance

List of Graduate School Programs and Degrees

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Berkeley Berkeley Academic Guide: Academic Guide 2023-24

Computational biology.

About the Program

Under the auspices of the Center for Computational Biology, the Computational Biology Graduate Group offers the PhD in Computational Biology as well as the Designated Emphasis in Computational and Genomic Biology, a specialization for doctoral students in associated programs. The PhD is concerned with advancing knowledge at the interface of the computational and biological sciences and is therefore intended for students who are passionate about being high functioning in both fields. The designated emphasis augments disciplinary training with a solid foundation in the different facets of genomic research and provides students with the skills needed to collaborate across disciplinary boundaries to solve a wide range of computational biology and genomic problems.

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Admission to the University

Applying for graduate admission.

Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. A complete list of graduate academic departments, degrees offered, and application deadlines can be found on the Graduate Division website .

Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application can be found on the Graduate Division website .

Admission Requirements

The minimum graduate admission requirements are:

A bachelor’s degree or recognized equivalent from an accredited institution;

A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and

Enough undergraduate training to do graduate work in your chosen field.

For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page . It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here .

Where to apply?

Visit the Berkeley Graduate Division application page .

Admission to the Program

Applicants for the Computational Biology PhD are expected to have a strong foundation in relevant stem fields, achieved by coursework in at least two computational biology subfields (including, but not limited to, advanced topics in biology, computer science, mathematics, statistics). Typical students admitted to the program have demonstrated outstanding potential as a research scientist and have clear academic aptitude in multiple disciplines, as well as excellent communication skills. This is assessed based on research experience, coursework & grades, essays, personal background, and letters of recommendation. Three letters of recommendation are required, but up to five can be submitted. The GRE is no longer accepted or used as part of the review (this includes both the general and subject exams). The program does *not* offer a Masters degree in Computational Biology.

Doctoral Degree Requirements

Normative time requirements, normative time to advancement: two years.

Please refer to the PhD page on the CCB website for the most up-to-date requirements and information.

Year 1 Students perform three laboratory rotations with the chief aim of identifying a research area and thesis laboratory. They also take courses to advance their knowledge in their area of expertise or fill in gaps in foundational knowledge. With guidance from the program, students are expected to complete six total graded courses by the end of the second year (not including the Doc Sem or Ethics course). Please see the program's website for more detailed course and curriculum requirements.

Year 2 Students attend seminars, complete course requirements, and prepare a dissertation prospectus in preparation for their PhD oral qualifying examination. With the successful passing of the orals, students select their thesis committee and advance to candidacy for the PhD degree.

Normative Time in Candidacy: Three years

Years 3 to 5 Students undertake research for the PhD dissertation under a three or four-person committee in charge of their research and dissertation. Students conduct original laboratory research and then write the dissertation based on the results of this research. On completion of the research and approval of the dissertation by the committee, the students are awarded the doctorate.

Total Normative Time: 5-5.5 years

Time to advancement, lab rotations.

Students conduct three 10-week laboratory rotations in the first year. The thesis lab, where dissertation research will take place, is chosen at the end of the third rotation in late April/early May.

Qualifying Examination

The qualifying examination will evaluate a student’s depth of knowledge in his or her research area, breadth of knowledge in fundamentals of computational biology, ability to formulate a research plan, and critical thinking. The QE prospectus will include a description of the specific research problem that will serve as a framework for the QE committee members to probe the student’s foundational knowledge in the field and area of research. Proposals will be written in the manner of an NIH-style grant proposal. The prospectus must be completed and submitted to the chair no fewer than four weeks prior to the oral qualifying examination. Students are expected to pass the qualifying examination by the end of the fourth semester in the program.

Time in Candidacy

Advancement.

After passing the qualifying exam by the end of the second year, students have until the beginning of the fifth semester to select a thesis committee and submit the Advancement to Candidacy paperwork to the Graduate Division.

Dissertation

Primary dissertation research is conducted in years 3-5/5.5. Requirements for the dissertation are decided in consultation with the thesis advisor and thesis committee members. To this end, students are required to have yearly thesis committee meetings with the committee after advancing to candidacy.

Dissertation Presentation/Finishing Talk

There is no formal defense of the completed dissertation; however, students are expected to publicly present a talk about their dissertation research in their final year.

Required Professional Development

Presentations.

All computational biology students are expected to attend the annual retreat, and will regularly present research talks there. They are also encouraged to attend national and international conferences to present research.

Computational biology students are required to teach for one or two semesters (either one semester at 50% (20hrs/wk) or two semesters at 25% (10hrs/wk)) and may teach more. The requirement can be modified if the student has funding that does not allow teaching.

Designated Emphasis Requirements

Curriculum/coursework.

Please refer to the DE page on the CCB website for the most up-to-date requirements and information.

The DE curriculum consists of one semester of the Doctoral Seminar in computational biology (CMPBIO 293, offered Fall & Spring) taken before the qualifying exam, plus three courses, one each from the three broad areas listed below, which may be independent from or an integral part of a student’s Associated Program. The three courses should be taken in different departments, only one of which may be the student’s home program. These requirements must be fulfilled with coursework taken with a grade of B or better while the student is enrolled as a graduate student at UC Berkeley. S/U graded courses do not count . See below for recommended coursework.

Students do not need to complete all of the course requirements prior to the application or the qualifying exam. The Doctoral Seminar does not need to be taken in order, ie either Fall or Spring are ok, but should be prior to or in the same semester as the Qualifying Exam. The DE will be rescinded if coursework has not been completed upon graduation (students should report their progress each year to the DE advisor, especially if they wish to change one of the courses they listed for the requirement).

• Computer Science and Engineering: A single course at the level of CS61A or higher will fulfill this requirement. Students can also take CS 88 (as an alternative to CS61A), though depending on their background, Data 8 may be necessary to complete this course. Students with a more advanced background are recommended to take a higher level CS course to fulfill the requirement.
• Biostatistics, Mathematics and Statistics: A single course at the level of Stat 131A, 133, 134, or 135 or higher will fulfill this requirement. Students with a more advanced background are recommended to take one of either Stat 201A & 201B or a higher level course to fulfill the requirement. Statistics or probability courses from other departments may be able to fulfill this requirement with prior approval of the program.
• Biology: please select an appropriate biology course from the list linked below (not up-to-date), or choose a course from current course listings.
• Computational Biology: CMPBIO C293, Doctoral Seminar, offered Fall & Spring.

More information, including a link to pre-approved courses, can be found on the CCB website .

Qualifying Examination and Dissertation

The qualifying examination and dissertation committees must include at least one (more is fine) Core faculty members from the Computational Biology Graduate Group. The faculty member(s) may serve any role on the committee from Chair to ASR. The Qualifying Examination must include examination of knowledge within the area of Computational and Genomic Biology. The Comp Bio Doctoral Seminar must be completed before the QE, as it will be important preparation for the exam.

Seminars & Retreat

Students must attend the annual Computational Biology Retreat (generally held in November) as well as regular CCB Seminar Series , or equivalent, as designated by the Curriculum Committee. Students are also strongly encouraged to attend or volunteer with program events during Orientation, Recruitment, Symposia, etc. Available travel funds will be dependent upon participation.

CMPBIO 201 Classics in Computational Biology 3 Units

Terms offered: Fall 2015, Fall 2014, Fall 2013 Research project and approaches in computational biology. An introducton to the diverse ways biological problems are investigated computationally through critical evaluation of the classics and recent peer-reviewed literature. This is the core course required of all Computational Biology graduate students. Classics in Computational Biology: Read More [+]

Rules & Requirements

Prerequisites: Acceptance in the Computational Biology Phd program; consent of instructor

Hours & Format

Fall and/or spring: 15 weeks - 1 hour of lecture and 2 hours of discussion per week

Additional Format: One hour of Lecture and Two hours of Discussion per week for 15 weeks.

Additional Details

Subject/Course Level: Computational Biology/Graduate

Grading: Letter grade.

Classics in Computational Biology: Read Less [-]

CMPBIO C210 Introduction to Quantitative Methods In Biology 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course provides a fast-paced introduction to a variety of quantitative methods used in biology and their mathematical underpinnings. While no topic will be covered in depth, the course will provide an overview of several different topics commonly encountered in modern biological research including differential equations and systems of differential equations, a review of basic concepts in linear algebra, an introduction to probability theory, Markov chains, maximum likelihood and Bayesian estimation, measures of statistical confidence, hypothesis testing and model choice, permutation and simulation, and several topics in statistics and machine learning including regression analyses, clustering, and principal component analyses. Introduction to Quantitative Methods In Biology: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Ability to calculate means and variances for a sample and relate it to expectations and variances of a random variable. Ability to calculate probabilities of discrete events using simple counting techniques, addition of probabilities of mutually exclusive events, multiplication of probabilities of independent events, the definition of conditional probability, the law of total probability, and Bayes’ formula, and familiarity with the use of such calculations to understand biological relationships. Ability to carry out various procedures for data visualization in R. Ability to classify states in discrete time Markov chains, and to calculate transition probabilities and stationary distributions for simple discrete time, finite state-space Markov chains, and an understanding of the modeling of evolutionary processes as Markov chains. Ability to define likelihood functions for simple examples based on standard random variables. Ability to implement simple statistical models in R and to use simple permutation procedures to quantify uncertainty. Ability to implement standard and logistic regression models with multiple covariates in R. Ability to manipulate matrices using multiplication and addition. Ability to model simple relationships between biological variables using differential equations. Ability to work in a Unix environment and manipulating files in Unix. An understanding of basic probability theory including some of the standard univariate random variables, such as the binomial, geometric, exponential, and normal distribution, and how these variables can be used to model biological systems. An understanding of powers of matrices and the inverse of a matrix. An understanding of sampling and sampling variance. An understanding of the principles used for point estimation, hypothesis testing, and the formation of confidence intervals and credible intervals. Familiarity with ANOVA and ability to implementation it in R. Familiarity with PCA, other methods of clustering, and their implementation in R. Familiarity with basic differential equations and their solutions. Familiarity with covariance, correlation, ordinary least squares, and interpretations of slopes and intercepts of a regression line. Familiarity with functional programming in R and/or Python and ability to define new functions. Familiarity with one or more methods used in machine learning/statistics such as hidden Markov models, CART, neural networks, and/or graphical models. Familiarity with python allowing students to understand simple python scripts. Familiarity with random effects models and ability to implement them in R. Familiarity with the assumptions of regression and methods for investigating the assumptions using R. Familiarity with the use of matrices to model transitions in a biological system with discrete categories.

Prerequisites: Introductory calculus and introductory undergraduate statistics recommended

Credit Restrictions: Students will receive no credit for INTEGBI C201 after completing INTEGBI 201. A deficient grade in INTEGBI C201 may be removed by taking INTEGBI 201, or INTEGBI 201.

Fall and/or spring: 15 weeks - 3 hours of lecture and 3 hours of laboratory per week

Additional Format: Three hours of lecture and three hours of laboratory per week.

Formerly known as: Integrative Biology 201

Also listed as: INTEGBI C201

Introduction to Quantitative Methods In Biology: Read Less [-]

CMPBIO C231 Introduction to Computational Molecular and Cell Biology 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022, Fall 2021, Fall 2020 This class teaches basic bioinformatics and computational biology, with an emphasis on alignment, phylogeny, and ontologies. Supporting foundational topics are also reviewed with an emphasis on bioinformatics topics, including basic molecular biology, probability theory, and information theory. Introduction to Computational Molecular and Cell Biology: Read More [+]

Prerequisites: BIO ENG 11 or BIOLOGY 1A (may be taken concurrently); and a programming course ( ENGIN 7 or COMPSCI 61A )

Credit Restrictions: Students will receive no credit for BIO ENG C231 after completing BIO ENG 231 . A deficient grade in BIO ENG C231 may be removed by taking BIO ENG 231 , or BIO ENG 231 .

Instructor: Holmes

Also listed as: BIO ENG C231

Introduction to Computational Molecular and Cell Biology: Read Less [-]

CMPBIO C249 Computational Functional Genomics 4 Units

Terms offered: Fall 2024, Fall 2023 This course provides a survey of the computational analysis of genomic data, introducing the material through lectures on biological concepts and computational methods, presentations of primary literature, and practical bioinformatics exercises. The emphasis is on measuring the output of the genome and its regulation. Topics include modern computational and statistical methods for analyzing data from genomics experiments: high-throughput RNA sequencing data , single-cell data, and other genome-scale measurements of biological processes. Students will perform original analyses with Python and command-line tools. Computational Functional Genomics: Read More [+]

Course Objectives: This course aims to equip students with practical proficiency in bioinformatics analysis of genomic data, as well as understanding of the biological, statistical, and computational underpinnings of this field.

Student Learning Outcomes: Students completing this course should have stronger programming skills, practical proficiency with essential bioinformatics methods that are applicable to genomics research, understanding of the statistics underlying these methods, and awareness of key aspects of genome function and challenges in the field of genomics.

Prerequisites: Math 54 or EECS 16A /B; CS 61A or another course in python; BioE 11 or Bio 1a; and BioE 131. Introductory statistics or data science is recommended

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week.

Instructor: Lareau

Also listed as: BIO ENG C249

Computational Functional Genomics: Read Less [-]

CMPBIO C256 Human Genome, Environment and Public Health 4 Units

Terms offered: Spring 2024, Spring 2023, Fall 2020 This introductory course will cover basic principles of human/population genetics and molecular biology relevant to molecular and genetic epidemiology. The latest methods for genome-wide association studies and other approaches to identify genetic variants and environmental risk factors important to disease and health will be presented. The application of biomarkers to define exposures and outcomes will be explored. Recent developments in genomics , epigenomics and other ‘omics’ will be included. Computer and wet laboratory work will provide hands-on experience. Human Genome, Environment and Public Health: Read More [+]

Prerequisites: Introductory level biology/genetics course, or consent of instructor. Introductory biostatistics and epidemiology courses strongly recommended

Credit Restrictions: Students will receive no credit for PB HLTH C256 after completing CMPBIO 156 . A deficient grade in PB HLTH C256 may be removed by taking CMPBIO 156 .

Fall and/or spring: 15 weeks - 2 hours of lecture and 2 hours of laboratory per week

Additional Format: Two hours of lecture and two hours of laboratory per week.

Instructors: Barcellos, Holland

Also listed as: PB HLTH C256

Human Genome, Environment and Public Health: Read Less [-]

CMPBIO C256A Human Genome, Environment and Human Health 3 Units

Terms offered: Spring 2017 This introductory course will cover basic principles of human/population genetics and molecular biology relevant to understanding how data from the human genome are being used to study disease and other health outcomes. The latest designs and methods for genome-wide association studies and other approaches to identify genetic variants, environmental risk factors and the combined effects of gene and environment important to disease and health will be presented. The application of biomarkers to define exposures and outcomes will be explored. The course will cover recent developments in genomics, epigenomics and other ‘omics’, including applications of the latest sequencing technology and characterization of the human microbiome. Human Genome, Environment and Human Health: Read More [+]

Prerequisites: Introductory level biology course. Completion of introductory biostatistics and epidemiology courses strongly recommended and may be taken concurrently

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week.

Also listed as: PB HLTH C256A

Human Genome, Environment and Human Health: Read Less [-]

CMPBIO C256B Genetic Analysis Method 3 Units

Terms offered: Prior to 2007 This introductory course will provide hands-on experience with modern wet laboratory techniques and computer analysis tools for studies in molecular and genetic epidemiology and other areas of genomics in human health. Students will also participate in critical review of journal articles. Students are expected to understand basic principles of human/population genetics and molecular biology, latest designs and methods for genome-wide association studies and other approaches to identify genetic variants, environmental risk factors and the combined effects of gene and environment important to human health. Students will learn how to perform DNA extraction, polymerase chain reaction and methods for genotyping, sequencing, and cytogenetics. Genetic Analysis Method: Read More [+]

Prerequisites: Introductory level biology course. Completion of introductory biostatistics and epidemiology courses strongly recommended and may be taken concurrently with permission. PH256A is a requirement for PH256B; they can be taken concurrently

Fall and/or spring: 15 weeks - 2-2 hours of lecture and 1-3 hours of laboratory per week

Additional Format: Two hours of lecture and one to three hours of laboratory per week.

Also listed as: PB HLTH C256B

Genetic Analysis Method: Read Less [-]

CMPBIO 275 Computational Biology Seminar/Journal Club 1 Unit

Terms offered: Fall 2024, Spring 2024, Fall 2023 This seminar course will cover a wide range of topics in the field of computational biology. The main goals of the course are to expose students to cutting edge research in the field and to prepare students for engaging in academic discourse with seminar speakers - who are often leaders in their fields. A selected number of class meetings will be devoted to the review of scientific papers published by upcoming seminar speakers and the other class meetings will be devoted to discussing other related articles in the field. The seminar will expose students to both the breadth and highest standards of current computational biology research. Computational Biology Seminar/Journal Club: Read More [+]

Repeat rules: Course may be repeated for credit without restriction.

Fall and/or spring: 15 weeks - 1 hour of seminar per week

Additional Format: One hour of seminar per week.

Grading: Offered for satisfactory/unsatisfactory grade only.

Computational Biology Seminar/Journal Club: Read Less [-]

CMPBIO 276 Algorithms for Computational Biology 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course will provide familiarity with algorithms and probabilistic models that arise in various computational biology applications, such as suffix trees, suffix arrays, pattern matching, repeat finding, sequence alignment, phylogenetics, hidden Markov models, gene finding, motif finding, linear/logistic regression, random forests, convolutional neural networks, genome-wide association studies, pathogenicity prediction, and sequence-to-epigenome prediction. Algorithms for Computational Biology: Read More [+]

Prerequisites: CompSci 70 AND CompSci 170, MATH 54 OR EECS 16A OR an equivalent linear algebra course

Repeat rules: Course may be repeated for credit with instructor consent.

Instructors: Song, Ioannidis

Algorithms for Computational Biology: Read Less [-]

CMPBIO 290 Special Topics - Computational Biology 1 - 4 Units

Terms offered: Fall 2022, Fall 2021, Spring 2018 This graduate-level course will cover various special topics in computational biology and the theme will vary from semester to semester. The course will focus on computational methodology, but also cover relevant biological applications. This course will be offered according to student demand and faculty availability. Special Topics - Computational Biology: Read More [+]

Prerequisites: Graduate standing in EECS, MCB, Computational Biology or related fields; or consent of the instructor

Fall and/or spring: 15 weeks - 1-3 hours of lecture per week

Additional Format: One to three hours of lecture per week for standard offering. In some instances, condensed special topics classes running from 2-10 weeks may also be offered usually to accommodate guest instructors. Total works hours will remain the same but more work in a given week will be required.

Special Topics - Computational Biology: Read Less [-]

CMPBIO 293 Doctoral Seminar in Computational Biology 2 Units

Terms offered: Fall 2024, Fall 2023, Spring 2023 This interactive seminar builds skills, knowledge and community in computational biology for first year PhD and second year Designated Emphasis students. Topics covered include concepts in human genetics/genomics, microbiome data analysis, laboratory methodologies and data sources for computational biology, workshops/instruction on use of various bioinformatics tools, critical review of current research studies and computational methods, preparation for success in the PhD program and career development. Faculty members of the graduate program in computational biology and scientists from other institutions will participate. Topics will vary each semester. Doctoral Seminar in Computational Biology: Read More [+]

Fall and/or spring: 15 weeks - 2 hours of seminar per week

Additional Format: Two hours of seminar per week.

Doctoral Seminar in Computational Biology: Read Less [-]

CMPBIO C293 Doctoral Seminar in Computational Biology 2 Units

Terms offered: Spring 2024, Fall 2022, Fall 2021 This interactive seminar builds skills, knowledge and community in computational biology for first year PhD and second year Designated Emphasis students. Topics covered include concepts in human genetics/genomics, microbiome data analysis, laboratory methodologies and data sources for computational biology, workshops/instruction on use of various bioinformatics tools, critical review of current research studies and computational methods, preparation for success in the PhD program and career development. Faculty members of the graduate program in computational biology and scientists from other institutions will participate. Topics will vary each semester. Doctoral Seminar in Computational Biology: Read More [+]

Instructors: Moorjani, Rokhsar

Also listed as: MCELLBI C296

CMPBIO 294A Introduction to Research in Computational Biology 2 - 12 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Closely supervised experimental or computational work under the direction of an individual faculty member; an introduction to methods and research approaches in particular areas of computational biology. Introduction to Research in Computational Biology: Read More [+]

Prerequisites: Standing as a Computational Biology graduate student

Fall and/or spring: 15 weeks - 2-20 hours of laboratory per week

Additional Format: Two to Twenty hours of Laboratory per week for 15 weeks.

Introduction to Research in Computational Biology: Read Less [-]

CMPBIO 294B Introduction to Research in Computational Biology 2 - 12 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Closely supervised experimental or computational work under the direction of an individual faculty member; an introduction to methods and research approaches in particular areas of computational biology. Introduction to Research in Computational Biology: Read More [+]

CMPBIO 295 Individual Research for Doctoral Students 1 - 12 Units

Terms offered: Summer 2024 10 Week Session, Summer 2023 10 Week Session, Summer 2022 10 Week Session Laboratory research, conferences. Individual research under the supervision of a faculty member. Individual Research for Doctoral Students: Read More [+]

Prerequisites: Acceptance in the Computational Biology PhD program; consent of instructor

Fall and/or spring: 15 weeks - 1-20 hours of laboratory per week

Summer: 10 weeks - 1.5-30 hours of laboratory per week

Additional Format: One to twenty hours of laboratory per week. One and one-half to thirty hours of laboratory per week for 10 weeks.

Individual Research for Doctoral Students: Read Less [-]

CMPBIO 477 Introduction to Programming for Bioinformatics Bootcamp 1.5 Unit

Terms offered: Prior to 2007 The goals of this course are to introduce students to Python, a simple and powerful programming language that is used for many applications, and to expose them to the practical bioinformatic utility of Python and programming in general. The course will allow students to apply programming to the problems that they face in the lab and to leave this course with a sufficiently generalized knowledge of programming (and the confidence to read the manuals) that they will be able to apply their skills to whatever projects they happen to be working on. Introduction to Programming for Bioinformatics Bootcamp: Read More [+]

Prerequisites: This is a graduate course and upper level undergraduate students can only enroll with the consent of the instructor

Summer: 3 weeks - 40-40 hours of workshop per week

Additional Format: Organized as a bootcamp, the ten-day course will include two sessions daily, each consisting of roughly two hours of lecture and up to three hours of hands on exercises.

Subject/Course Level: Computational Biology/Other professional

Introduction to Programming for Bioinformatics Bootcamp: Read Less [-]

Contact Information

Computational biology graduate group.

574 Stanley Hall

Phone: 510-642-0379

Fax: 510-666-3399

[email protected]

Director, CCB

Elizabeth Purdom

[email protected]

Executive Director, CCB

Phone: 510-666-3342

[email protected]

Graduate Program Manager

574 Stanley Hall, MC #3220

[email protected]

Head Graduate Advisor and Chair for the PhD & DE

John Huelsenbeck

[email protected]

CCB DE Advising

[email protected]

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Computational Biology PhD

Under the auspices of the Center for Computational Biology, the Computational Biology Graduate Group offers the PhD in Computational Biology as well as the Designated Emphasis in Computational and Genomic Biology, a specialization for doctoral students in associated programs. The PhD is concerned with advancing knowledge at the interface of the computational and biological sciences and is therefore intended for students who are passionate about being high functioning in both fields. The designated emphasis augments disciplinary training with a solid foundation in the different facets of genomic research and provides students with the skills needed to collaborate across disciplinary boundaries to solve a wide range of computational biology and genomic problems.

Contact Info

[email protected]

108 Stanley Hall

Berkeley, CA 94720

At a Glance

Department(s)

Computational Biology Graduate Group

Admit Term(s)

Application Deadline

November 30, 2023

Degree Type(s)

Doctoral / PhD

Degree Awarded

GRE Requirements

Joint Carnegie Mellon-University of Pittsburgh Ph.D. Program in Computational Biology

Carl kingsford elected 2024 iscb fellow, similar genetic elements underlie vocal learning in mammals, logan and pfenning labs publish in nature communications, cpcb faculty and students win first place in cache challenge.

The Joint   C MU- P itt Ph.D. Program in   C omputational   B iology (CPCB)    provides interdisciplinary training in using quantitative and computational approaches to tackle scientific questions that lie at the interface of the life, physical, engineering, and computer sciences.  CPCB trainees are taught and mentored by leading experts at two of the foremost computer science and biomedical research institutions in the world.

The program provides students with interdisciplinary training in various fields of computational biology: Cellular and Systems Modeling, Computational Structural Biology, Bioimage Informatics, and Computational Genomics.  CPCB students also benefit from numerous professional development opportunities available at both host institutions.  Together, the CPCB program positions our students to be leaders in this exciting field of biology and has prepared our graduates to go on to successful careers in both academia, industry, and beyond!

The program currently has 91 students, who are taught and mentored by leading experts at two of the foremost computer science and biomedical research institutions in the world. Students receive interdisciplinary training from 57 core faculty and 57 affiliated faculty, representing over 30 departments and centers in the universities.

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Genomics and Computational Biology, PhD

Genomics and computational biology are now at the center of biomedical research. These disciplines take a holistic approach to ask about the origins, functions, and interactions of whole systems, using both experimental and theoretical work. Therefore, these studies require knowledge, skills, and, most importantly, synthesis and integration of biology, computer science, mathematics, statistics, and engineering.

This synthesis and integration requires a new generation of scientists that thrives in cross-disciplinary research. This can include molecular, cellular, and organismal biology (including genetics), mathematics, statistics, chemistry, and engineering. The goal of the GCB program is to train students that are experts in one or more of these disciplines and well versed in the others. We provide a comprehensive training program in Genomics and Computational Biology that gives students a broad foundation in the biological and quantitative sciences along with practical experience in computational and experimental genomics. The knowledge gained in this program will serve students in their careers as technology progresses.

For more information: https://www.med.upenn.edu/gcb/

View the University’s Academic Rules for PhD Programs .

Required Courses 

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2024 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

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Computational Biology

Computational Biology Program

The computational biology ph.d. program is training the next generation of computational scientists to tackle research using the big genomic, image, remote sensing, clinical, and real world data that are transforming the biological sciences..

The graduate field of Computational Biology offers Ph.D. degrees in the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological systems.

Computation has become essential to biological research. Genomic databases, protein databanks, MRI images of the human brain, and remote sensing data on landscapes contain unprecedented amounts of detailed information that are transforming almost all of biology. The computational biologist must have skills in mathematics and computation as well as in biology. A key goal in training is to develop the ability to relate biological processes to computational models.

The field provides interdisciplinary training and research opportunities in a range of subareas of computational biology including comparative and functional genomics, systems biology, molecular and protein networks, population genomics and genetics, bioinformatics, model system genomics, agricultural genomics, and medical genomics.

Students majoring in computational biology are expected to obtain a broad, interdisciplinary knowledge of fundamental principles in biology, computational science, and mathematics. But because the field covers a wide range of areas, it would be unrealistic to expect a student to master each facet in detail. Instead, students choose from specific subareas of study: They are expected to develop competence in at least one specific subdomain of biology and in relevant subareas of computational science and mathematics.

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Welcome to the MIT Computational and Systems Biology PhD Program (CSB)

The Ph.D. program seeks to train a new breed of quantitative biologists who can take advantage of technologies at the leading edge of science and engineering to tackle fundamental and applied problems in biology. Our students acquire: (i) a background in modern molecular/cell biology; (ii) a foundation in quantitative/engineering disciplines to enable them to create new technologies as well as apply existing methods; and (iii) exposure to subjects emphasizing the application of quantitative approaches to biological problems.  Our program and courses emphasize the logic of scientific discovery rather than mastering a specific set of skills or facts.  The program includes teaching experience during one semester of the second year.  It prepares students with the tools needed to succeed in a variety of academic and non-academic careers.

The program is highly selective with typical class sizes 8 to 10 students. About half of our graduate students are women, about one-quarter are international students, and about 10% are under-represented minorities.

Students complete most coursework during the first year, while exploring research opportunities through 1- or 2-month research rotations.  A faculty academic advisor assigned in the first year provides guidance and advice. Students choose a research advisor in spring or early summer of year 1 and develop a Ph.D. research project in with their advisor and input from a thesis committee chosen by the student.

Average time to graduation is 5½ years. 

The Program in CSB is committed to increasing opportunities for under-represented minority graduate students and students who have experienced financial hardship or disability.

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QUANTITATIVE AND

COMPUTATIONAL BIOLOGY

PhD PROGRAM

We prepare students to develop or innovatively apply novel, effective, and efficient computational methods capable of answering complex problems through a rigorous curriculum grounded in a focus on statistics, algorithms, and biology.

Why Choose Our P.h.D. Program?

Ph.D. QUICK LINKS

A Ph.D. in Computational Biology and Bioinformatics from the Dornsife College of Letter, Arts and Sciences at USC offers its students a truly unique graduate experience: ​

A long history in Computational Biology training

A distinguished body of Alumni

A strong focus in methodological development

Close ties with experimental biology

A distinguished list of faculty

A strong research program

Our QCB Department is an academic unit, and not a virtual program

Our Philosophy: A Strong Focus in Method Development and Applications

We prepare students to be able to develop and innovatively apply novel, effective, and efficient computational methods to address the challenges arisen from emerging data.

We train our students to have a solid foundation of statistics, algorithms, and biology with a rigorous curriculum, and build their ability to analyze data and formulate problems by research projects.

This training prepares our students for a variety of careers

Join A Collaborative Working Environment

We are the PhD Program of USC's Department of Quantitative and Computational Biology, located in two modern research buildings shared with wet-lab experimental biologists, as well as chemists, physicists, and engineers.

We have close collaborations with faculty at the Dornsife College of Letters, Arts and Sciences, the Keck School of Medicine, the Viterbi School of Engineering, and other Schools across the university.

An Independent Academic Department, Not A Virtual Program

Most Computational Biology training programs in the world collect faculties from different departments, and build a virtual program. In contrast all faculty in our program belong to the same academic unit, physically located in the same two buildings (we also have several, affiliated faculty from other departments).

With an exceptionally strong research program, our graduate students over the past decade have published multiple significant research papers, including articles in Science, Cell, Nature, Nature Biotechnology, Nature Methods, PNAS, and Genome Research.

A World Class Faculty

Our Department of Quantitative and Computational Biology currently has 15 tenured and tenure track, 2 teaching and 2 Emeritus core faculty members. We also have 20 joint faculty members from the Dornsife College of Letter, Arts and Sciences, the Viterbi School of Engineering and the Keck School of Medicine, and other USC Schools.

Our faculty members have won many prestigious awards: The QCB faculty include a Nobel Laureate, four members of the US National Academy of Sciences, one member of the US National Academy of Engineering, two fellows of the Royal Society, one member of the French Academy of Sciences, one member of the Chinese Academy of Sciences, and five AAAS Fellows. 

A Distinguished Body of Alumni

Our program in Computational Biology and Bioinformatics is the world’s oldest training program in Computational Biology. In 2022 we have celebrated 40 years of Computational Biology at USC with the USC Computational Biology Symposium 2022. (link conference website here) At this conference, the we also celebrated the new QCB Department and Professor Michael Waterman’s 80th birthday. 

Over the past 40 years, many prominent and leading computational biologists have been trained at USC. A list of recent graduates of our program is available HERE . Become part of our QCB family!

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Information for prospective Ph.D. students in Computational Biology or Bioinformatics

The Ph.D. programs in Computational Biology at Johns Hopkins University span four Departments and a wide range of research topics. Our programs provide interdisciplinary training in computational and quantitative approaches to scientific problems that include questions in genomics, medicine, genome engineering, sequencing technology, molecular biology, genetics, and others.

Our students are actively involved in high-profile research, and have developed very widely-used bioinformatics software systems such as Bowtie , Tophat , and Cufflinks . and the more-recent systems HISAT and Stringtie (for RNA-seq alignment and assembly) and Kraken (for metagenomic sequence analysis). The work they do with Hopkins faculty prepares them to go on to postdoctoral and tenure track faculty positions at top-ranked universities including (in recent years) Harvard, the University of Washington, Carnegie Mellon, the University of Maryland, and Brown.

Students in computational biology at Hopkins can enroll in one of four different Ph.D. programs. These include Biomedical Engineering, ranked #1 in the nation; Biostatistics, also ranked #1 in the nation; Biology, ranked #6 in the nation; and the rapidly growing Computer Science Department, ranked #23 in the nation. Hopkins is also ranked #4 in the nation in Bioinformatics, a ranking that just started appearing in 2022.

CCB faculty have appointments in each of these programs, and some of us maintain appointments in multiple programs. To determine which program fits your interests and background, browse the course lists below. Each program has a separate application process; please apply specifically to the departments you're interested in. Applications to multiple programs are permitted, but if you're not certain, we encourage you to contact potential faculty advisors before you apply. Wherever you apply, make it clear that your interest is Computational Biology.

Sample Course Offerings for Ph.D. students in Computational Biology

Department of biomedical engineering, whiting school of engineering.

The Johns Hopkins Department of Biomedical Engineering (BME), widely regarded as the top program of its kind in the world and ranked #1 in the nation by U.S. News , is dedicated to solving important scientific problems at the intersection of multiple disciplines and that have the potential to make a significant impact on medicine and health. At the intersection of inquiry and discovery, the department integrates biology, medicine, and engineering and draws upon the considerable strengths and talents of the Johns Hopkins Schools of Engineering and Medicine. See the BME Ph.D. program website for many details.

Department of Computer Science, Whiting School of Engineering

The faculty represent a broad spectrum of disciplines encompassing core computer science and many cross-disciplinary areas including Computational Biology and Medicine, Information Security, Machine Learning, Data Intensive Computing, Computer-Integrated Surgery, and Natural Language Processing.

Ph.D. program

A total of 8 courses are required, and a typical load is 3 courses per semester. See the CS Department website for details. For a look at courses that might be included in Ph.D. training, see this page , though note that it is not a comprehensive list. For the Computer Science Ph.D., 2 out of the required 8 classes can be taken outside the Department. These may include any of the courses in the BME, Biostatistics, and Biology programs listed on this page.

Department of Biostatistics, Bloomberg School of Public Health

Johns Hopkins Biostatistics is the oldest department of its kind in the world and has long been considered as one of the best. In 2022, it was ranked #1 in the nation by U.S. News .

All students in the Biostatistics Ph.D. program have to complete the core requirements:

• A two-year sequence on biostatistical methodology (140.751-756)
• A two-year sequence on probability and the foundations and theory of statistical science (550.620-621, 140.673-674, 140.771-772);
• Principles of Epidemiology (340.601)

In addition, students in computational biology might take:

• 140.776.01 Statistical Computing (3 credits)
• 140.638.01 Analysis of Biological Sequences (3 credits)
• 140.644.01 Statistica machine learning: methods, theory, and applications (4 credits)
• 140.688.01 Statistics for Genomics (3 credits)

Further courses might include 2-3 courses in Computer Science, BME, or Biology listed on this page.

Department of Biology, Krieger School of Arts and Sciences

The Hopkins Biology Graduate Program, founded in 1876, is the oldest Biology graduate school in the country. People like Thomas Morgan, E. B. Wilson, Edwin Conklin and Ross Harrison, were part of the initial graduate classes when the program was first founded. Hopkins is ranked #6 in the nation in Biological Sciences by U.S. News

Quantitative and computational biology are an integral part of the CMDB training program. During the first semester students attend Quantitative Biology Bootcamp, a one week intensive course in using computational tools and programming for biological data analysis. Two of our core courses - Graduate Biophysical Chemistry and Genomes and Development - each have an associated computational lab component.

Ph.D. in Cell, Molecular, Developmental Biology, and Biophysics (CMDB):

The CMDB core includes the following courses:

• 020.607 Quantitative Biology Bootcamp
• 020.674 Graduate Biophysical Chemistry
• 020.686 Advanced Cell Biology
• 020.637 Genomes and Development
• 020.668 Advanced Molecular Biology
• 020.606 Molecular Evolution
• 020.620 Stem Cells
• 020.630 Human Genetics
• 020.640 Epigenetics & Chromosome Dynamics
• 020.650 Eukaryotic Molecular Biology
• 020.644 RNA

Students in computational biology can use their electives to take more computationally intensive courses. You have considerable flexibility to design a program of study with your Ph.D. advisor.

The Center for Computational Biology at Johns Hopkins University

Yale Computational Biology and Bioinformatics

Graduate program.

CBB is part of the Yale combined program in the  Biological and Biomedical Sciences (BBS) . BBS is an umbrella program that incorporates a number of different areas of biological research. Students apply to the CBB track of the BBS program and are part of this track for their first year of studies. After their first year, students join the CBB interdepartmental graduate program and work towards a degree in Computational Biology and Bioinformatics. Alternatively, students may join any of the other departments associated with BBS if they find that another department better fits their interest after the first year.

CBB offers a Ph.D. with an en-route Masters’ degree; a terminal Masters’ program is offered with a Computational Biology or Biomedical Informatics concentration as well. For more information about these degrees, visit the Curriculum  page. For more information about applying to these programs, visit the Application Information  page.

There are a variety of social and career events held throughout the year, including Research in Progress (RIP) talks, journal clubs and seminars. Check the Calendar page for upcoming events.

There are also a wide range of initiatives at Yale that relate to CBB, including (but not limited to):

• Biomedical Informatics Research Training Grant from the National Library of Medicine
• Biomedical Informatics & Data Science (BIDS)
• “West Campus” Initiatives including the Yale Center for Genome Analysis (Shrikant Mane) and Yale Systems Biology Institute (Andre Levchenko)
• ENCODE & PsychENCODE projects (Mark Gerstein, Nenad Sestan)
• Department of Pathology Informatics Group (Steven Kleinstein, Yuval Kluger)
• MCDB informatics faculty (Thierry Emonet, Damon Clark)
• Integrated Graduate Program in Physical and Engineering Biology (Corey O’Hern, Lynne Regan, and Simon Mochrie)

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Computational Biology PhD Programs

The Computational Biology Program offers a PhD degree in Computational Biology. The graduate program, one of the top in the U.S. in Bioinformatics and Computational Biology, involves faculty who are recognized leaders in the fields of structure prediction of proteins and protein complexes, molecular simulation, systems biology, genomics, protein design, and drug discovery. The program faculty and students actively engage in international community-wide activities in bioinformatics and computational biology. For more information, see Computational Biology Program /  The Center for Computational Biology . The University of Kansas has world-class credentials in life sciences and information technology. The program complements on-going efforts to expand these areas of research and education at  The University of Kansas .

If you have any questions regarding the PhD program, contact Program Director Dr. Ilya Vakser at  [email protected] .

Computational Biology

Our interdisciplinary M.S. in Computational Biology program is designed to provide students with expertise in the leading quantitative methods underlying modern biomedical science. The program is an in-depth response to the ever-growing need for computational methods and mathematical models in processing, analyzing, and interpreting the vast amounts of biological data generated by high-throughput techniques. Computer simulations are required to understand and predict the dynamics of complex biological systems. Precision medicine, drug development, and cancer research are only a few among the many thriving fields increasingly relying on quantitative genomics, bioinformatics, and systems biology.

The M.S. in Computational Biology (MS-CB) presents a unique, rigorous training program, equipping students with theoretical understanding and practical mastery of state-of-the-art applications of computational approaches in biology and medicine. Our faculty from Weill Cornell Medicine, Sloan-Kettering Institute, and Cornell Tech are world-class leaders in computational biology research and applications.

Upon graduation, with extensive training and field-specific, curricular workshops in career development, students will be prepared to launch successful professional careers at the forefront of data analytics, bioinformatics, and computer modeling, for example in the pharmaceutical or biotech industries. Likewise, for those interested in pursuing further education in computational biology at the PhD level, this degree will attest to their preparation and enhance their competitiveness.

Our  curriculum  is highly interdisciplinary and includes training in cutting-edge bioinformatics, statistics, machine learning, computation and simulation, quantitative biology, and genomics. The training emphasizes hands-on computer labs and practical skills to prepare students for careers beyond the classroom.

Program features include:

• 18 months duration, full-time study
• cohesive interdisciplinary educational program
• individual mentored research project
• career development training

Please  see here  for a complete list of faculty

Tuition, Fees and Scholarships

Please refer to the  student services website  for program-specific details on tuition and fees. Please note that this tuition cost and fees are set for the current academic year and are subject to change.

A small number of partial scholarships are available. Applicants applying by the priority deadline are automatically considered for these merit-based scholarships.

Program Requirements

Applicants must hold a bachelor’s degree in science or engineering. Applicants must have completed undergraduate level coursework in calculus, linear algebra, probability theory or statistics, and computer programming.

We seek applications from students with diverse undergraduate degrees and welcome applications from talented individuals of all backgrounds. 

All application forms and supporting documents can be  submitted online . You will be asked to submit or upload:

• Personal Statement describing your background and specific interest in the MS-CB program.
• Résumé/C.V.
• Three letters of recommendation. Letters must be submitted electronically as instructed through the online application.
• Transcripts from all previously attended colleges and universities
• Domestic Transcripts - Unofficial transcripts from U.S. institutions may be submitted for application review. Official transcripts will be requested from accepted students prior to matriculation.
• If using WES, please select the WES Basic Course-by-Course evaluation and choose "Cornell University - Manhattan NY" as the recipient with "Weill Graduate School of Medical Sciences" as the School/Division 
• Evaluations are accepted only from  current members of the National Association of Credit Evaluation Services (NACES) . Official course-by-course evaluations are required for application review.
• $80 application fee
• Results of the General Graduate Record (GRE) examination are  optional . The Institution Code Number is 2119.
• Scores from the  Test of English as a Foreign Language (TOEFL) ,  International English Language Testing System (IELTS) , or  Duolingo English Test . Test scores are valid for two years after the test date. To see if you qualify for an exemption, see below.
• To submit your official TOEFL scores, please go to  http://www.ets.org/toefl  and request your scores to be sent to Weill Cornell Graduate School using code 2119. Please monitor your application to ensure that your scores are populated by ETS.  
• IELTS results must be submitted directly via e-delivery to “Weill Cornell Graduate School of Medical Sciences.”
• Results for the Duolingo English Test, applicants must submit their results directly through Duolingo to “Weill Cornell Graduate School of Medical Sciences".

Application Timeline & Deadlines

The application site for Fall 2024 admission is open. 

We are still accepting applications for the Fall 2024 class. We are operating with a rolling admissions process at this point and encourage you to submit your application as early as possible to avoid potential seat capacity or timing restrictions.  

Final deadline for applications: May 1, 2024. 

English Language Proficiency Exam Exemptions

The English language proficiency requirement may be waived if an applicant meets at least one of the following criteria:

• Citizenship/Permanent Residency
• If the applicant is a citizen or permanent resident of the United States or its territories (e.g., Puerto Rico), or a citizen of the United Kingdom, Ireland, Australia, New Zealand, or Canada, they are exempt.
• Applicants who are citizens of all other countries, including India, Pakistan, the Philippines, Hong Kong, Singapore, etc. are not exempt and must submit English language proficiency exam scores.
• English-Language Instruction
• Applicants who, at the time of enrollment, have studied in full-time status for at least two academic years within the last  five  years in the United States, the United Kingdom, Ireland, Australia, or New Zealand, or with English language instruction in Canada or South Africa, are exempt.
• Applicants must submit a transcript that shows they studied in one of the approved locations, and that the academic program was at least two years in length.
• Even if English was the language of instruction of the course or institution, it must have been in one of the eligible locations, otherwise the applicant is not exempt from the requirement.

Prospective Student Events

We're always working on putting events together. Be sure to check back soon for more event listings.

Faculty Stories

• Aguiar-Pulido, Vanessa
• Bao, Zhirong
• Bendall, Matthew
• Berger, Michael
• Betel, Doron
• Brady, Nicholas
• Christini, David
• Davis, Melissa
• Dundar, Friederike
• Elemento, Olivier
• Hajirasouliha, Iman
• Imielinski, Marcin
• Kentsis, Alex
• Khurana, Ekta
• Krogh-Madsen, Trine
• Krumsiek, Jan
• Landau, Dan
• Laughney, Ashley
• Lee, Guinevere
• Leslie, Christina
• Mason, Christopher
• Nixon, Douglas
• Papaemmanuil, Elli
• Sboner, Andrea
• Schultz, Nikolaus
• Skrabanek, Luce
• Tilgner, Hagen
• Ventura, Andrea

Courses and Required Curricular Components

• Analysis of Next-Generation Sequencing Data
• Career Development in Computational Biology
• Cellular and Molecular Biology
• Computational Biology Research
• Data Structures and Algorithms for Computational Biology
• Dynamic Models in Biology
• Functional Interpretation of High-Throughput Data
• MS-CB Thesis Research
• MS-CB Thesis Research Exploration 1&2
• Quantitative Genomics and Genetics

Student Stories

I have enjoyed exploring a bunch of different areas of research, specifically those to which I was completely new. Everyone here has been extremely supportive and there has been a lot of room for growth. Overall it's been really fun figuring out what I'd like to do moving forward!

Weill Cornell is unique in how focused it is on medical science research. There is no other institution where I can so easily find professors to collaborate with for clinical research. In the short time I have spent in the program, I have meaningfully contributed to several projects that will be published.

Student Handbook

To view the MSCB Student Handbook, click here .

  Contact Information

  Trine Krogh-Madsen, PhD, Director 413 E. 69th St, Box 190 New York, NY 10065 (646) 962 - 5392 [email protected]

Lucia Li , Program Coordinator 1300 York Ave, Box 65 New York, NY 10065 [email protected]

Weill Cornell Medicine Graduate School of Medical Sciences 1300 York Ave. Box 65 New York, NY 10065 Phone: (212) 746-6565 Fax: (212) 746-8906

The Ph.D. in Bioinformatics and Computational Biology (BCB) is granted for planning, execution, and defense of original research resulting in significant contributions to the discipline’s body of knowledge. Moreover, the BCB Ph.D. program also requires didactic coursework to prepare the student for research success. Student progress is primarily assessed by: (a) satisfactory coursework performance, (b) the Qualifying Examination, (c) the Dissertation Proposal, and (d) the Dissertation Defense. Courses and the Qualifying Examination are used to ensure that the student has sufficient breadth of knowledge. The Dissertation Proposal is used to ensure that the scope of dissertation research is important, that the plan is well thought out and that the student has sufficient skills and thoughtfulness needed for success. The Dissertation Defense is used to assess the outcomes of the dissertation research, and whether or not the plan agreed upon by the Dissertation Committee has been appropriately followed.

Admissions Requirements

The Ph.D. in Bioinformatics and Computational Biology admits students on a competitive basis. Preference is given to applicants with strong credentials and appropriate undergraduate and/or professional preparation.

• A baccalaureate degree from an institution accredited by an accepted accrediting body. Admission requirements for Bioinformatics track will include an adequate preparation in chemistry, biology, mathematics (preferably statistics), and computer science. Strong candidates may be allowed to make up deficiencies in some areas at the discretion of the Bioinformatics admissions subcommittee.
• Evidence of scholarly and creative activity, including publication list; awards; results in national or international contests, and the like
• A minimum GPA of 3.0 (on a 4.0 scale)
• Excellent GRE scores
• Three letters of reference from professionals working in the applicant’s field of interest that addresses the applicant’s previous experience and potential to do research
• Personal statement. Please include answers to the following questions:

1. What area(s) of research in Bioinformatics and Genomics are you most interested in? Which faculty members in the department would you most like to work with in order to further pursue these areas? Please discuss at least 3 different faculty, but no more than five.

2. Why have you chosen to apply to this particular program? What are your career goals, and how will a degree from this program help you achieve those goals?

3. What kind of prior experience do you have that you feel has prepared you for graduate-level research? If you have done research as an undergraduate, please tell us about the questions you were aiming to answer, your role in the research project, and what the outcome was. If you have not done research as an undergraduate, tell us instead about other projects or work efforts that you led, and what you did to accomplish the project goals.

4. What kind of technical skills do you have that are most relevant to the Ph.D. program in Bioinformatics and Genomics?

5. What would you describe as your biggest personal strengths? How do you feel these strengths will increase your chance of success in a Ph.D. program?

The GRE can be waived if one of the following conditions are met:

• Applicant obtained, or will complete prior to enrollment, a M.S. degree from a college or university in the U.S. accredited by an accepted accrediting body, with a GPA of 3.2 or higher, in a STEM major related to Bioinformatics and Genomics
• Applicant obtained, or will complete prior to enrollment, a B.S. degree from a college or university in the U.S. accredited by an accepted accrediting body, with a GPA of 3.4 or higher, in a STEM major related to Bioinformatics and Genomics

Please note: a waiver of the GRE requirement does not constitute an offer of admission into the program.  

Degree Requirements

The BCB program requires 72 credit hours in 8000-level BINF courses, or prior approved substitutions.  All students must complete two Research Rotations in the first year of the program; each provides a semester of faculty supervised research experience to supplement regular course offerings.  Students must complete the Core Courses prior to taking the Qualifying Exam.  In consultation with their Academic Advisor and/or Program Director, students frequently also take an appropriate selection of the Gateway Courses in order to be prepared for the Core Courses.  For example, an incoming student with a Computer Science background would be expected to take BINF 8100    and BINF 8101   , but not BINF 8111   .  All students must complete the Core Courses prior to taking the Qualifying Examination.  Each Ph.D. student must complete two Research Rotations in the first year.  Each Research Rotation provides a semester of faculty supervised research experience to supplement regular course offerings.  Graduate Research Seminar is taken every semester until the semester following advancement to candidacy.  Finally, many additional Elective Courses are available, but are not explicitly required.

Gateway Courses

(as needed, based on student’s background)

• BINF 8100 - Biological Basis of Bioinformatics (3)
• BINF 8101 - Energy and Interaction in Biological Modeling (3)
• BINF 8111 - Bioinformatics Programming I (3)

Core Courses

• BINF 8112 - Bioinformatics Programming II (3)
• BINF 8200 - Statistics for Bioinformatics (3)
• BINF 8201 - Molecular Sequence Analysis (3)
• Students must choose one additional elective at the 8200 level.

Research Rotations

• BINF 8911 - Bioinformatics Research Rotation I (2)
• BINF 8912 - Bioinformatics Research Rotation II (2)

Graduate Research Seminar

• BINF 8600 - Bioinformatics Seminar (1) (Must be taken every semester until the semester following advancement to candidacy)

Research Hours

• BINF 8991 - Doctoral Dissertation Research (1 to 9) (Must take a minimum of 18 hours)

Responsible Conduct of Research

Select one of the following:

• GRAD 8302 - Responsible Conduct of Research (2)
• BINF 8151 - Professional Communications (1)

Molecular Biophysics Concentration - Optional (8 credit hours)

• PHYS 6108 - Biophysics (3)
• or   OPTI 8000 - Selected Topics in Optics (3)
• PHYS 6204 - Methods of Molecular Modeling and Simulation in Physics (3)
• or   BINF 8311 - Biophysical Modeling (3)
• PHYS 6610 - Biophysics Seminar (1) (to be taken twice)

Elective Courses

Any graduate level BINF prefix course may be taken as a pre-approved elective. Other courses may be taken with department approval.

Degree Total = 72 Credit Hours

Qualifying examination.

Prior to defining a research topic, students are required to pass a Qualifying Examination to demonstrate proficiency in bioinformatics and computational biology, as well as competence in fundamentals common to the field. The Qualifying Examination must be passed prior to the fifth semester of residence. It is composed of both written and oral components that emphasize material covered in the Core Courses listed above.

Dissertation Proposal

Each student must present and defend a Ph.D. Dissertation Research Proposal within two semesters of passing the Qualifying Examination.  The Dissertation Proposal defense will be conducted by the student’s Dissertation Committee, and will be open to faculty and students. The proposal must address a significant, original and substantive piece of research. The proposal must include sufficient preliminary data and a timeline such that the Dissertation Committee can assess its feasibility.

Dissertation

Each student must complete a well-designed original research contribution, as agreed upon by the student and Dissertation Committee at the Dissertation Proposal. The Ph.D. Dissertation is a written document describing the research and its results, and their context in the sub-discipline. The Dissertation Defense is a public presentation of the findings of the research, with any novel methods that may have been developed to support the conclusions. The student must present the Dissertation and defend its findings publicly, and in a private session with the Dissertation Committee immediately thereafter.

M.S. in Computational Biology

A Joint Computational Biology Department and Department of Biological Sciences   Program

MSCB mission statement

The MSCB program seeks to train the world’s best Computational Biologists at the Master’s level. The curriculum provides both breadth and depth of training in Computational Biology and is built on a solid foundation of Biology, Computer Science, Statistics, and Machine Learning (Data Sciences). Interested students are also given opportunities to pursue research. Our graduates are prepared for rewarding jobs in industry or to pursue their doctoral degrees at top universities.

Bioinformatics.  Personalized medicine. Sequenced microbial genomes. Progress in gene therapy. Improvements in nutrition.

Making sense of these advances in biomedical science and of the knowledge explosion in domains such as genetics, drug design, neuroscience, and environmental health requires both a sophisticated understanding of biological questions and powerful analytical tools to solve them. The integrated discipline of computational biology/bioinformatics represents the application of modern computer science, statistics, and mathematics to exploring biological and biomedical problems. The Department of Biological Sciences in the Mellon College of Science and the Ray and Stephanie Lane Computational Biology Department in the School of Computer Science combine their world-class strengths in computer science and biology with the strong tradition of interdisciplinary research at Carnegie Mellon into a unique training program in this emerging field.

Coursework consists of Foundation Courses, Background Courses, and Breadth & Depth Courses in a wide array of disciplines such as computer science, machine learning, math, statistics, biology, chemistry, biomedical engineering, and information management. Students have the option of conducting in-depth research in addition to coursework, and are also encouraged to seek external internships after their first year. Students pursue this degree full-time and complete the program in 3-4 semesters.

Students who have completed the program have gone on to work in a wide range of industries in biotech and pharma as well as goverment and academic institutions. Recent graduates have been employed at companies and institutes such as the J. Craig Venter Institute, Thermo Fisher Scientific, Philips Research, and Broad Institute of MIT and Harvard, to name a few. Other graduates have gone on to pursue Ph.D. degrees at a number of top universities around the world.

How to Apply

The CMU Rales Fellow Program is dedicated to developing a diverse community of STEM leaders from underrepresented and underresourced backgrounds by eliminating cost as a barrier to education. Learn more about this program for master's and Ph.D. students. Learn more

Computational Biology Twitter

Biological sciences twitter, departments.

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Graduate School

Biochemistry (ph.d.), biochemistry (ph.d.) | graduate.

Our Biochemistry doctoral students are at the forefront of biochemical research and molecular medicine, examining biological mechanisms underlying human disease; they are finding new ways to detect and attack diseases and immunological disorders like cancer, neurological disorders, and cardiovascular disease.

Graduates of the Ph.D. in Biochemistry program at Howard's Graduate School are prepared for careers at top research universities and senior-level research positions in biomedical and related industries. The program's key strengths in molecular microbiology, proteomics and genetics, bioinformatics, and drug design and discovery make us a nexus for collaborative investigations between biochemistry researchers and clinicians. You'll learn to apply biochemical techniques, including NMR spectroscopy, crystallography, and single-molecule methods as well as contemporary approaches to cell culture and genetic analysis to answer key questions about the pathogenesis of specific diseases and the development of effective drug therapies. You'll also enjoy the close mentorship of faculty who are committed to your professional development. Our faculty are experts in several areas of biochemistry, including analysis of molecular structure, proteomics and genetics, tumor biology, structural biology, enzymology, RNA catalysis, stress response, and RNA modification. As you advance in the program, you'll become increasingly involved in laboratory research and the critical analysis of biochemical literature. Our graduate seminar series offers a venue to present your early-stage research. Students may pursue a dual M.D./Ph.D. degree.

Program Snapshot

      ❱  72 credit hours        ❱  Full-time       ❱  On-campus format       ❱  Degree: Ph.D.       ❱  Dual degree: M.D./Ph.D.

Application Deadlines

Spring 2024 entry:         ❱  No spring entry 

Fall 2024 entry:         ❱  Dec. 1, 2023 (early deadline)       ❱  Feb. 15, 2024 (priority deadline)       ❱  Apr. 15, 2024 (final deadline)

Applicants should submit their applications as early as possible for earlier consideration of departmental funding opportunities. Applicants have until the final deadline to apply. However, applications will be reviewed on a rolling basis throughout the admissions cycle. 

Dr. Zaki Sherif

Dr. matthew george, jr., angela wilson, program details.

• Degree Classification: Graduate
• Related Degrees: M.D. / Ph.D., Ph.D.

Admission Requirements

Application for admission.

• Online GradCAS application
• Statement of purpose/ Statement of academic interest ( 500-1,000 words )
• GRE scores  not required
• Official transcripts sent to GradCAS
• 3 letters of recommendation
• Bachelor's degree from an accredited college or university or the international equivalent 
• Resume or Curriculum Vitae
• Autobiographical statement ( 500-750 words )

GRE Required?

Gre preferred minimums.

• GRE Verbal Reasoning: N/A
• GRE Quantitative Reasoning: N/A
• GRE Analytical Writing: N/A

GPA Required Minimums

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The following course prerequisites are required. Applicants are required to have at least a B average in these prerequisites.   No expiration date for recommended prerequisites.

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• Calculus  (college-level course, 3 semester credit hrs)

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40 Facts About Elektrostal

Written by Lanette Mayes

Modified & Updated: 21 May 2024

Reviewed by Jessica Corbett

Elektrostal is a vibrant city located in the Moscow Oblast region of Russia. With a rich history, stunning architecture, and a thriving community, Elektrostal is a city that has much to offer. Whether you are a history buff, nature enthusiast, or simply curious about different cultures, Elektrostal is sure to captivate you.

This article will provide you with 40 fascinating facts about Elektrostal, giving you a better understanding of why this city is worth exploring. From its origins as an industrial hub to its modern-day charm, we will delve into the various aspects that make Elektrostal a unique and must-visit destination.

So, join us as we uncover the hidden treasures of Elektrostal and discover what makes this city a true gem in the heart of Russia.

Key Takeaways:

• Elektrostal, known as the “Motor City of Russia,” is a vibrant and growing city with a rich industrial history, offering diverse cultural experiences and a strong commitment to environmental sustainability.
• With its convenient location near Moscow, Elektrostal provides a picturesque landscape, vibrant nightlife, and a range of recreational activities, making it an ideal destination for residents and visitors alike.

Known as the “Motor City of Russia.”

Elektrostal, a city located in the Moscow Oblast region of Russia, earned the nickname “Motor City” due to its significant involvement in the automotive industry.

Home to the Elektrostal Metallurgical Plant.

Elektrostal is renowned for its metallurgical plant, which has been producing high-quality steel and alloys since its establishment in 1916.

Boasts a rich industrial heritage.

Elektrostal has a long history of industrial development, contributing to the growth and progress of the region.

Founded in 1916.

The city of Elektrostal was founded in 1916 as a result of the construction of the Elektrostal Metallurgical Plant.

Located approximately 50 kilometers east of Moscow.

Elektrostal is situated in close proximity to the Russian capital, making it easily accessible for both residents and visitors.

Known for its vibrant cultural scene.

Elektrostal is home to several cultural institutions, including museums, theaters, and art galleries that showcase the city’s rich artistic heritage.

A popular destination for nature lovers.

Surrounded by picturesque landscapes and forests, Elektrostal offers ample opportunities for outdoor activities such as hiking, camping, and birdwatching.

Hosts the annual Elektrostal City Day celebrations.

Every year, Elektrostal organizes festive events and activities to celebrate its founding, bringing together residents and visitors in a spirit of unity and joy.

Has a population of approximately 160,000 people.

Elektrostal is home to a diverse and vibrant community of around 160,000 residents, contributing to its dynamic atmosphere.

Boasts excellent education facilities.

The city is known for its well-established educational institutions, providing quality education to students of all ages.

A center for scientific research and innovation.

Elektrostal serves as an important hub for scientific research, particularly in the fields of metallurgy , materials science, and engineering.

Surrounded by picturesque lakes.

The city is blessed with numerous beautiful lakes , offering scenic views and recreational opportunities for locals and visitors alike.

Well-connected transportation system.

Elektrostal benefits from an efficient transportation network, including highways, railways, and public transportation options, ensuring convenient travel within and beyond the city.

Famous for its traditional Russian cuisine.

Food enthusiasts can indulge in authentic Russian dishes at numerous restaurants and cafes scattered throughout Elektrostal.

Home to notable architectural landmarks.

Elektrostal boasts impressive architecture, including the Church of the Transfiguration of the Lord and the Elektrostal Palace of Culture.

Offers a wide range of recreational facilities.

Residents and visitors can enjoy various recreational activities, such as sports complexes, swimming pools, and fitness centers, enhancing the overall quality of life.

Provides a high standard of healthcare.

Elektrostal is equipped with modern medical facilities, ensuring residents have access to quality healthcare services.

Home to the Elektrostal History Museum.

The Elektrostal History Museum showcases the city’s fascinating past through exhibitions and displays.

A hub for sports enthusiasts.

Elektrostal is passionate about sports, with numerous stadiums, arenas, and sports clubs offering opportunities for athletes and spectators.

Celebrates diverse cultural festivals.

Throughout the year, Elektrostal hosts a variety of cultural festivals, celebrating different ethnicities, traditions, and art forms.

Electric power played a significant role in its early development.

Elektrostal owes its name and initial growth to the establishment of electric power stations and the utilization of electricity in the industrial sector.

Boasts a thriving economy.

The city’s strong industrial base, coupled with its strategic location near Moscow, has contributed to Elektrostal’s prosperous economic status.

Houses the Elektrostal Drama Theater.

The Elektrostal Drama Theater is a cultural centerpiece, attracting theater enthusiasts from far and wide.

Popular destination for winter sports.

Elektrostal’s proximity to ski resorts and winter sport facilities makes it a favorite destination for skiing, snowboarding, and other winter activities.

Promotes environmental sustainability.

Elektrostal prioritizes environmental protection and sustainability, implementing initiatives to reduce pollution and preserve natural resources.

Home to renowned educational institutions.

Elektrostal is known for its prestigious schools and universities, offering a wide range of academic programs to students.

Committed to cultural preservation.

The city values its cultural heritage and takes active steps to preserve and promote traditional customs, crafts, and arts.

Hosts an annual International Film Festival.

The Elektrostal International Film Festival attracts filmmakers and cinema enthusiasts from around the world, showcasing a diverse range of films.

Encourages entrepreneurship and innovation.

Elektrostal supports aspiring entrepreneurs and fosters a culture of innovation, providing opportunities for startups and business development.

Offers a range of housing options.

Elektrostal provides diverse housing options, including apartments, houses, and residential complexes, catering to different lifestyles and budgets.

Home to notable sports teams.

Elektrostal is proud of its sports legacy, with several successful sports teams competing at regional and national levels.

Boasts a vibrant nightlife scene.

Residents and visitors can enjoy a lively nightlife in Elektrostal, with numerous bars, clubs, and entertainment venues.

Promotes cultural exchange and international relations.

Elektrostal actively engages in international partnerships, cultural exchanges, and diplomatic collaborations to foster global connections.

Surrounded by beautiful nature reserves.

Nearby nature reserves, such as the Barybino Forest and Luchinskoye Lake, offer opportunities for nature enthusiasts to explore and appreciate the region’s biodiversity.

Commemorates historical events.

The city pays tribute to significant historical events through memorials, monuments, and exhibitions, ensuring the preservation of collective memory.

Promotes sports and youth development.

Elektrostal invests in sports infrastructure and programs to encourage youth participation, health, and physical fitness.

Hosts annual cultural and artistic festivals.

Throughout the year, Elektrostal celebrates its cultural diversity through festivals dedicated to music, dance, art, and theater.

Provides a picturesque landscape for photography enthusiasts.

The city’s scenic beauty, architectural landmarks, and natural surroundings make it a paradise for photographers.

Connects to Moscow via a direct train line.

The convenient train connection between Elektrostal and Moscow makes commuting between the two cities effortless.

A city with a bright future.

Elektrostal continues to grow and develop, aiming to become a model city in terms of infrastructure, sustainability, and quality of life for its residents.

In conclusion, Elektrostal is a fascinating city with a rich history and a vibrant present. From its origins as a center of steel production to its modern-day status as a hub for education and industry, Elektrostal has plenty to offer both residents and visitors. With its beautiful parks, cultural attractions, and proximity to Moscow, there is no shortage of things to see and do in this dynamic city. Whether you’re interested in exploring its historical landmarks, enjoying outdoor activities, or immersing yourself in the local culture, Elektrostal has something for everyone. So, next time you find yourself in the Moscow region, don’t miss the opportunity to discover the hidden gems of Elektrostal.

Q: What is the population of Elektrostal?

A: As of the latest data, the population of Elektrostal is approximately XXXX.

Q: How far is Elektrostal from Moscow?

A: Elektrostal is located approximately XX kilometers away from Moscow.

Q: Are there any famous landmarks in Elektrostal?

A: Yes, Elektrostal is home to several notable landmarks, including XXXX and XXXX.

Q: What industries are prominent in Elektrostal?

A: Elektrostal is known for its steel production industry and is also a center for engineering and manufacturing.

Q: Are there any universities or educational institutions in Elektrostal?

A: Yes, Elektrostal is home to XXXX University and several other educational institutions.

Q: What are some popular outdoor activities in Elektrostal?

A: Elektrostal offers several outdoor activities, such as hiking, cycling, and picnicking in its beautiful parks.

Q: Is Elektrostal well-connected in terms of transportation?

A: Yes, Elektrostal has good transportation links, including trains and buses, making it easily accessible from nearby cities.

Q: Are there any annual events or festivals in Elektrostal?

A: Yes, Elektrostal hosts various events and festivals throughout the year, including XXXX and XXXX.

Elektrostal's fascinating history, vibrant culture, and promising future make it a city worth exploring. For more captivating facts about cities around the world, discover the unique characteristics that define each city . Uncover the hidden gems of Moscow Oblast through our in-depth look at Kolomna. Lastly, dive into the rich industrial heritage of Teesside, a thriving industrial center with its own story to tell.

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Out of the Centre

Savvino-storozhevsky monastery and museum.

Zvenigorod's most famous sight is the Savvino-Storozhevsky Monastery, which was founded in 1398 by the monk Savva from the Troitse-Sergieva Lavra, at the invitation and with the support of Prince Yury Dmitrievich of Zvenigorod. Savva was later canonised as St Sabbas (Savva) of Storozhev. The monastery late flourished under the reign of Tsar Alexis, who chose the monastery as his family church and often went on pilgrimage there and made lots of donations to it. Most of the monastery’s buildings date from this time. The monastery is heavily fortified with thick walls and six towers, the most impressive of which is the Krasny Tower which also serves as the eastern entrance. The monastery was closed in 1918 and only reopened in 1995. In 1998 Patriarch Alexius II took part in a service to return the relics of St Sabbas to the monastery. Today the monastery has the status of a stauropegic monastery, which is second in status to a lavra. In addition to being a working monastery, it also holds the Zvenigorod Historical, Architectural and Art Museum.

Belfry and Neighbouring Churches

Located near the main entrance is the monastery's belfry which is perhaps the calling card of the monastery due to its uniqueness. It was built in the 1650s and the St Sergius of Radonezh’s Church was opened on the middle tier in the mid-17th century, although it was originally dedicated to the Trinity. The belfry's 35-tonne Great Bladgovestny Bell fell in 1941 and was only restored and returned in 2003. Attached to the belfry is a large refectory and the Transfiguration Church, both of which were built on the orders of Tsar Alexis in the 1650s.  

To the left of the belfry is another, smaller, refectory which is attached to the Trinity Gate-Church, which was also constructed in the 1650s on the orders of Tsar Alexis who made it his own family church. The church is elaborately decorated with colourful trims and underneath the archway is a beautiful 19th century fresco.

Nativity of Virgin Mary Cathedral

The Nativity of Virgin Mary Cathedral is the oldest building in the monastery and among the oldest buildings in the Moscow Region. It was built between 1404 and 1405 during the lifetime of St Sabbas and using the funds of Prince Yury of Zvenigorod. The white-stone cathedral is a standard four-pillar design with a single golden dome. After the death of St Sabbas he was interred in the cathedral and a new altar dedicated to him was added.

Under the reign of Tsar Alexis the cathedral was decorated with frescoes by Stepan Ryazanets, some of which remain today. Tsar Alexis also presented the cathedral with a five-tier iconostasis, the top row of icons have been preserved.

Tsaritsa's Chambers

The Nativity of Virgin Mary Cathedral is located between the Tsaritsa's Chambers of the left and the Palace of Tsar Alexis on the right. The Tsaritsa's Chambers were built in the mid-17th century for the wife of Tsar Alexey - Tsaritsa Maria Ilinichna Miloskavskaya. The design of the building is influenced by the ancient Russian architectural style. Is prettier than the Tsar's chambers opposite, being red in colour with elaborately decorated window frames and entrance.

At present the Tsaritsa's Chambers houses the Zvenigorod Historical, Architectural and Art Museum. Among its displays is an accurate recreation of the interior of a noble lady's chambers including furniture, decorations and a decorated tiled oven, and an exhibition on the history of Zvenigorod and the monastery.

Palace of Tsar Alexis

The Palace of Tsar Alexis was built in the 1650s and is now one of the best surviving examples of non-religious architecture of that era. It was built especially for Tsar Alexis who often visited the monastery on religious pilgrimages. Its most striking feature is its pretty row of nine chimney spouts which resemble towers.

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The Unique Burial of a Child of Early Scythian Time at the Cemetery of Saryg-Bulun (Tuva)

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Pages:  379-406

In 1988, the Tuvan Archaeological Expedition (led by M. E. Kilunovskaya and V. A. Semenov) discovered a unique burial of the early Iron Age at Saryg-Bulun in Central Tuva. There are two burial mounds of the Aldy-Bel culture dated by 7th century BC. Within the barrows, which adjoined one another, forming a figure-of-eight, there were discovered 7 burials, from which a representative collection of artifacts was recovered. Burial 5 was the most unique, it was found in a coffin made of a larch trunk, with a tightly closed lid. Due to the preservative properties of larch and lack of air access, the coffin contained a well-preserved mummy of a child with an accompanying set of grave goods. The interred individual retained the skin on his face and had a leather headdress painted with red pigment and a coat, sewn from jerboa fur. The coat was belted with a leather belt with bronze ornaments and buckles. Besides that, a leather quiver with arrows with the shafts decorated with painted ornaments, fully preserved battle pick and a bow were buried in the coffin. Unexpectedly, the full-genomic analysis, showed that the individual was female. This fact opens a new aspect in the study of the social history of the Scythian society and perhaps brings us back to the myth of the Amazons, discussed by Herodotus. Of course, this discovery is unique in its preservation for the Scythian culture of Tuva and requires careful study and conservation.

Keywords: Tuva, Early Iron Age, early Scythian period, Aldy-Bel culture, barrow, burial in the coffin, mummy, full genome sequencing, aDNA

Information about authors: Marina Kilunovskaya (Saint Petersburg, Russian Federation). Candidate of Historical Sciences. Institute for the History of Material Culture of the Russian Academy of Sciences. Dvortsovaya Emb., 18, Saint Petersburg, 191186, Russian Federation E-mail: [email protected] Vladimir Semenov (Saint Petersburg, Russian Federation). Candidate of Historical Sciences. Institute for the History of Material Culture of the Russian Academy of Sciences. Dvortsovaya Emb., 18, Saint Petersburg, 191186, Russian Federation E-mail: [email protected] Varvara Busova  (Moscow, Russian Federation).  (Saint Petersburg, Russian Federation). Institute for the History of Material Culture of the Russian Academy of Sciences.  Dvortsovaya Emb., 18, Saint Petersburg, 191186, Russian Federation E-mail:  [email protected] Kharis Mustafin  (Moscow, Russian Federation). Candidate of Technical Sciences. Moscow Institute of Physics and Technology.  Institutsky Lane, 9, Dolgoprudny, 141701, Moscow Oblast, Russian Federation E-mail:  [email protected] Irina Alborova  (Moscow, Russian Federation). Candidate of Biological Sciences. Moscow Institute of Physics and Technology.  Institutsky Lane, 9, Dolgoprudny, 141701, Moscow Oblast, Russian Federation E-mail:  [email protected] Alina Matzvai  (Moscow, Russian Federation). Moscow Institute of Physics and Technology.  Institutsky Lane, 9, Dolgoprudny, 141701, Moscow Oblast, Russian Federation E-mail:  [email protected]

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