phd position machine learning data science

Machine Learning - CMU

Phd program in machine learning.

Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be leaders in both industry and academia.

Understanding the most effective ways of using the vast amounts of data that are now being stored is a significant challenge to society, and therefore to science and technology, as it seeks to obtain a return on the huge investment that is being made in computerization and data collection. Advances in the development of automated techniques for data analysis and decision making requires interdisciplinary work in areas such as machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining, etc.

The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning.  For questions and concerns, please   contact us .

The PhD program is a full-time in-person committment and is not offered on-line or part-time.

PhD Requirements

Requirements for the phd in machine learning.

• Completion of required courses , (6 Core Courses + 1 Elective)
• Mastery of proficiencies in Teaching and Presentation skills.
• Successful defense of a Ph.D. thesis.

Teaching Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.

Conference Presentation Skills During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Speaking Skills committee to attend and evaluate it.

Research It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.

Master of Science in Machine Learning Research - along the way to your PhD Degree.

Other Requirements In addition, students must follow all university policies and procedures .

Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs): The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s).  It must include:

• At least one MLD Core Faculty member
• At least one additional MLD Core or Affiliated Faculty member
• At least one External Member, usually meaning external to CMU
• A total of at least four members, including the advisor who is the committee chair

Financial Support

Application Information

For applicants applying in Fall 2024 for a start date of August 2025 in the Machine Learning PhD program, GRE Scores are STRONGLY RECOMMENDED. The committee uses GRE scores to gauge quantitative skills, and to a lesser extent, also verbal skills.

Proof of English Language Proficiency If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo.  We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored. We do not issue waivers for non-native speakers of English.   In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university.  We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States.  No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Submit valid, recent scores:   If as described above you are required to submit proof of English proficiency, your TOEFL, IELTS or Duolingo test scores will be considered valid as follows: If you have not received a bachelor’s degree in the U.S., you will need to submit an English proficiency score no older than two years. (scores from exams taken before Sept. 1, 2023, will not be accepted.) If you are currently working on or have received a bachelor's and/or a master's degree in the U.S., you may submit an expired test score up to five years old. (scores from exams taken before Sept. 1, 2019, will not be accepted.)

Graduate Online Application

• Admissions application opens September 4, 2024
• Early Application Deadline – November 20, 2024 (3:00 p.m. EST)
• Final Application Deadline - December 11, 2024 (3:00 p.m. EST)

• Working at Maastricht University

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NYU Center for Data Science

Harnessing Data’s Potential for the World

PhD in Data Science

An NRT-sponsored program in Data Science

• Areas & Faculty
• Admission Requirements
• Medical School Track
• NRT FUTURE Program

Advances in computational speed and data availability, and the development of novel data analysis methods, have birthed a new field: data science. This new field requires a new type of researcher and actor: the rigorously trained, cross-disciplinary, and ethically responsible data scientist. Launched in Fall 2017, the pioneering CDS PhD Data Science program seeks to produce such researchers who are fluent in the emerging field of data science, and to develop a native environment for their education and training. The CDS PhD Data Science program has rapidly received widespread recognition and is considered among the top and most selective data science doctoral programs in the world. It has recently been recognized by the NSF through an NRT training grant.

The CDS PhD program model rigorously trains data scientists of the future who (1) develop methodology and harness statistical tools to find answers to questions that transcend the boundaries of traditional academic disciplines; (2) clearly communicate to extract crisp questions from big, heterogeneous, uncertain data; (3) effectively translate fundamental research insights into data science practice in the sciences, medicine, industry, and government; and (4) are aware of the ethical implications of their work.

Our programmatic mission is to nurture this new generation of data scientists, by designing and building a data science environment where methodological innovations are developed and translated successfully to domain applications, both scientific and social. Our vision is that combining fundamental research on the principles of data science with translational projects involving domain experts creates a virtuous cycle: Advances in data science methodology transform the process of discovery in the sciences, and enable effective data-driven governance in the public sector. At the same time, the demands of real-world translational projects will catalyze the creation of new data science methodologies. An essential ingredient of such methodologies is that they embed ethics and responsibility by design.

These objectives will be achieved by a combination of an innovative core curriculum, a novel data assistantship mechanism that provides training of skills transfer through rotations and internships, and communication and entrepreneurship modules. Students will be exposed to a wider range of fields than in more standard PhD programs while working with our interdisciplinary faculty. In particular, we are proud to offer a medical track for students eager to explore data science as applied to healthcare or to develop novel theoretical models stemming from medical questions.

In short, the CDS PhD Data Science program prepares students to become leaders in data science research and prepares them for outstanding careers in academia or industry. Successful candidates are guaranteed financial support in the form of tuition and a competitive stipend in the fall and spring semesters for up to five years.* We invite you to learn more through our webpage or by contacting  [email protected] .

*The Ph.D. program also offers students the opportunity to pursue their study and research with Data Science faculty based at NYU Shanghai. With this opportunity, students generally complete their coursework in New York City before moving full-time to Shanghai for their research. For more information, please visit the NYU Shanghai Ph.D. page .

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Best Doctorates in Machine Learning: Top PhD Programs, Career Paths, and Salaries

If you want to take your career in machine learning to the next level, you might be considering enrolling in one of the best PhDs in machine learning. However, it can be hard to figure out which program is right for you, how to fulfill all the requirements, or how to secure the right funding opportunities so you can continue your education in this field.

This comprehensive guide will look at the best options for a machine learning PhD, both in-person and online. We’ll also discuss the best machine learning jobs and how to get them with this type of degree, as well as the average PhD in machine learning salary you can earn upon graduation.

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What is a phd in machine learning.

A PhD in machine learning is a research-intensive degree program that helps students further their education in machine learning. A machine learning PhD is a doctorate degree that involves coursework, qualifying exams, and oral examinations. Professors and members of faculty also work closely with students to help them develop a strong dissertation throughout their degree program.

Students interested in pursuing a PhD in machine learning should have already completed a bachelor’s degree in a relevant field. They also need to have completed a master’s degree , or commit to completing it along the way.

How to Get Into a Machine Learning PhD Program: Admission Requirements

The admission requirements to get into a machine learning PhD program typically include filling out an application form and submitting an application fee, academic transcripts from your undergraduate degree, two to three recommendation letters, a statement of purpose, GRE scores, a resume, writing sample, and English proficiency test scores for international students.

Each school’s website will have a detailed breakdown of all the requirements needed for the application process. Some schools may require you to pay an application fee, have a minimum GPA score, and take the Graduate Record Examination (GRE), although most schools have waived this requirement until 2023.

You will need two or three recommendation letters for your PhD application. The recommendation letter should be from faculty members and colleagues familiar with your work. Part of the application process is a statement of purpose, which is an essay that should tell the admission committee why you want to pursue a PhD in Machine Learning.

PhD in Machine learning Admission Requirements

• Application form
• Application fee
• College transcripts
• Minimum GPA of 3.0 (varies)
• Two to three recommendation letters
• Statement of purpose
• Writing sample
• English proficiency test (only for non-native English speakers)

Machine Learning PhD Acceptance Rates: How Hard Is It to Get Into a PhD Program in Machine Learning?

It is hard to get into a PhD program in machine learning. Prestigious schools are usually very selective and have a low admission rate ranging between four and 30 percent. For example, Harvard University has an admission rate of  four percent, so make sure you prepare a strong application and have a high GPA if you want to get into Harvard or another highly-reputable university.

However, not all PhD programs are extremely selective. For instance, institutions in the University of California system have higher acceptance rates, such as 34.4 percent. To improve your chances of acceptance, you can ask a friend or mentor to look over your PhD application. You should also apply to more than one program.

How to Get Into the Best Universities

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Best PhDs in Machine Learning: In Brief

Best universities for machine learning phds: where to get a phd in machine learning.

The best universities for machine learning PhDs include Carnegie Mellon University, Georgia Tech, and University of Washington. These schools can help you earn your machine learning PhD. If you’re wondering where you can get a PhD in machine learning, the list below discusses 10 excellent programs, along with their essential details.

Carnegie Mellon University was founded in 1900. It is known for its high-quality graduate programs in engineering, artificial intelligence (AI), and computer science. There are about 29 graduate degree programs offered at Carnegie Mellon University’s graduate school. Students and faculty conduct open and restricted research in four main areas, including AI, learning sciences, robotics, and neuroscience.

PhD in Machine Learning

The PhD in Machine Learning at Carnegie Mellon University requires students to take six core courses and one elective course. This research-focused degree program requires students to present and defend a thesis by the end of the program.

During this program, students need to work as teaching assistants for two semesters and will complete a presentation to show adequate presentation skills to the Speaking Skills committee. Common courses for this program include an introduction to machine learning, intermediate statistics, and regression analysis.

PhD in Machine Learning Overview

• Program Length: 5 years
• Acceptance Rate: 17%
• Tuition and Fees: $645/unit
• PhD Funding Opportunities: Graduate assistantships, scholarships, and grants

PhD in Machine Learning Admission Requirements

• GRE (recommended)
• TOEFL (for international applicants)
• Recommendation letters
• High level of knowledge in computer science and math

Georgia Institute of Technology is a reputable university founded in 1885. It is known for its excellent STEM majors, of which 86 percent of students are enrolled. It offers many graduate degree programs to its 25,011 graduate students, but the most well-known programs are in electrical and computer engineering, computer science, and mechanical engineering.

The PhD in Machine Learning at Georgia Institute of Technology will teach you excellent machine learning techniques through machine learning courses. Students need to complete four core courses, five elective courses, responsible conduct of research course, and three doctoral minors.

Typical courses for this PhD program include machine learning theory and methods, advanced theory, and computing and optimization. This program consists of many research hours and requires PhD students to complete the defense of a dissertation. Students also need to complete a qualifying exam.

• Program Length: 4 years
• Acceptance Rate: 21%
• Tuition and Fees: $586/credit (in state); $1,215/unit (out of state)
• PhD Funding Opportunities: Federal loans, private loans, federal work-study program
• Minimum GPA of 3.0
• Three letters of recommendation
• IELTS minimum score of 7.5 or higher for non-native speakers
• TOEFL minimum score of 100 or higher for non-native speakers
• GRE (optional)

Harvard University is a highly reputable and well-known private research university founded in 1636. It currently has about 33,276 students enrolled in undergraduate degrees, graduate degrees, and certificate programs. Harvard University has 12 graduate schools and a fantastic faculty, of which members have received Nobel prizes in chemistry, medicine, physics, literature, peace, and economic sciences.

PhD in Computer Science

The machine learning PhD program at Harvard University teaches students about the interaction of computation with the world and computation fundamentals. Students will work with highly-rated faculty members conducting research in programming languages, machine learning, and artificial intelligence during this excellent program. As they move through their program, students will learn about connecting computer science to other fields while they interact with lawyers, scientists, and engineers.

PhD in Computer Science Overview

• Acceptance Rate: 4%
• Tuition and Fees: $50,928/year
• PhD Funding Opportunities: Grants, fellowships, traineeships, research assistantships, and teaching fellowships

PhD in Computer Science Admission Requirements

• Transcripts
• At least one recommendation letter
• Show English proficiency (for non-native English speakers)

Northwestern University was launched in 1851 and is one of the top research universities in the country. Its more than 50 research centers focus on topics like nanotechnology, neuroscience, biotechnology, and drug discovery.

Currently, Northwestern university has over 13,000 grad students enrolled in its 173 graduate degree and certificate programs. Northwestern University is known for its fantastic business, education, and materials engineering degree programs.

PhD in Computer Science and Learning Sciences

The machine learning PhD program at Northwestern University is research-driven and helps students understand and build a connection between research on computation and learning. Students can choose between many different areas of study, including machine learning and programming language design.

To complete this program, there should be apparent relevance in your research between computer science and the learning science in your field of study, such as machine learning. You must also complete a qualifying exam, research projects, and a PhD dissertation. Courses include Machine Learning, Foundations of Learning Science, and Artificial Intelligence Programming.

PhD in Computer Science and Learning Sciences Overview

• Program Length: 4-9 years
• Acceptance Rate: 7%
• Tuition and Fees: $18,689/quarter for programs with 8 or fewer quarters; $4,672/quarter for more than 8 quarters
• PhD Funding Opportunities: Assistantships, grants, and fellowships

PhD in Computer Science and Learning Sciences Admission Requirements

• Online application form
• Academic transcripts
• GRE scores (temporarily not required, but still recommended)
• TOEFL scores (for international applicants) 

Tulane University was launched in 1834 and is in the top two percent of research universities in the US. Tulane University conducts research in bio-innovation, health, energy, and the environment. It offers over 200 graduate degrees to over 5,000 grad students.

Students at Tulane University graduate school can pursue PhDs in computer science, environmental health studies, economics, and more. This University offers excellent funding opportunities such as fellowships and stipends.  

The PhD in Computer science at Tulane University is a research-intensive program. Students must conduct research in a specific depth area such as machine learning, artificial intelligence, or data science. Students who specialize in machine learning will research machine learning techniques, theory of applications, machine learning systems, and algorithms.

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Topics covered during this graduate program include algorithms, machine learning, and computer networks. Students also need to take three research courses. Students will do a qualifying oral exam during this program, complete a prospectus presentation, and a PhD dissertation in their preferred specialization, such as machine learning.

• Program Length: 4-7 years
• Acceptance Rate: 9.73%
• Tuition and Fees: $1,831/credit; $35,088/year with 9 credits per semester 
• PhD Funding Opportunities: Scholarships, fellowships, and stipends
• University transcripts
• Statement of Purpose
• GRE test scores
• TOEFL scores (for international applicants)

University of California Irvine is a public land-grant university established in the 1960s as part of the University of California system. It is a research-focused institution and boasts eight  Nobel Prize winners among its alumni. The graduate school offers over 100 graduate programs. This university offers many different PhDs, including bioengineering, machine learning and data science, and mechanical engineering.

The PhD in Computer Science at University of California Irvine helps students learn computer science fundamentals and essential machine learning skills. This program involves a research project. Students need to choose a research topic such as machine learning and artificial intelligence, scientific computing, or any other research topic listed on the website. 

Students need to complete at least 47 units during their program and maintain a 3.5 GPA. Courses for this degree include Machine Learning, Machine Learning and Data Mining, and Analysis of Algorithms. Before the end of the program, students will complete a candidacy exam, submit a dissertation plan, complete a final exam, and defend their dissertations. 

• Program Length: 6-7 years
• Acceptance Rate: 28.96%
• Tuition and Fees: $18,037/year (in state); $33,139/year (out of state)
• PhD Funding Opportunities: Fellowships, graduate employment, research assistantships, and training grants
• English proficiency test scores (for international applicants)

University of California San Diego traces its roots back to 1960 and had its first enrollments in 1964. It offers over 200 degree programs at the undergraduate and graduate levels. It is a research-focused institution that conducts research in a variety of fields, from robotics and climate to microbiomes.

PhD in Machine Learning and Data Science

The PhD in Machine Learning and Data Science program teaches students essential machine learning techniques to help them further or start their careers in machine learning and artificial intelligence . During this graduate program, students need to complete 36 credit hours. They will conduct an in-depth research project, a preliminary exam, and a qualifying exam.

At the end of the PhD, students need to submit and defend a doctoral thesis. They are allowed to consider the faculty and choose a research advisor that fits their research style and goals. The research advisor will support the student through their PhD from start to finish. Courses included in this degree are Linear Algebra & Application, Deep Learning & Applications, Machine Learning for Image Processing, and Statistical Learning.

PhD in Machine Learning and Data Science Overview

• Program Length: 6-8 years
• Acceptance Rate: 34.3%
• Tuition and Fees: $ 11,442/year 
• PhD Funding Opportunities: Fellowships

PhD in Machine Learning and Data Science Admission Requirements

• GRE test scores (recommended)
• English proficiency test (for international applicants)
• Three recommendation letters
• High school and college transcripts

University of Pennsylvania is a research-driven university based in Philadelphia. It opened its doors to students in 1751. It prides itself on research and encourages students to conduct research during their studies. This university has twelve graduate schools that offer graduate degrees and certificates. Some of the fields for PhD level studies include biochemistry, economics, and materials science and engineering.

PhD in Computer and Information Science

The PhD in Computer and Information Science at the University of Pennsylvania has a specialization called Machine Learning + X, allowing students to choose machine learning and one other specialization to focus on throughout their programs. For example, you could choose to do a Machine Learning + Computer Architecture specialization.

This degree requires specific courses, a preliminary exam, a teaching assistantship, a defense proposal, a defense of your dissertation, and a submission of your thesis. This PhD will help students gain new machine learning skills and experience in machine learning.

PhD in Computer and Information Science Overview

• Tuition and Fees: $19,919/year for the first eight semesters; $1,836 flat rate after the first eight semesters
• PhD Funding Opportunities: Fellowships, teacher assistantships, and research assistantships

PhD in Computer and Information Science Admission Requirements

• Personal statement
• Unofficial academic transcripts
• Three official recommendation letters
• GRE scores (optional until 2023, but still recommended)

This public research university was established in 1895 and is known for its high-quality doctoral research. University of Texas at Arlington has more than 174 graduate degrees and other graduate study options. New and current students can pursue a PhD in different fields like computer science, civil engineering, and mathematics. 

The PhD in Computer Science offered by University of Texas at Arlington allows students to choose a study track. There are eight options, but students interested in machine learning should choose the intelligent systems track, which covers machine learning methods, neural networks, parallel AI, and more.

Throughout this degree program, students will complete 18 hours of coursework and complete two comprehensive exams, one of which is a dissertation proposal. They will also submit a final dissertation defense before being awarded their PhD.

• Program Length: 4-5 years
• Acceptance Rate: Not stated
• Tuition and Fees: $11,044/year (in state); $23,486/year (out of state)
• PhD Funding Opportunities: Teacher’s assistantships, research assistantships, fellowships, grants, and scholarships
• College transcripts 

University of Washington is a highly reputable school located in Washington that started conducting classes in 1861. It is known for its high-quality research and boasts that seven of its researchers have won Nobel prizes in physics, physiology, and medicine.

New and current students at University of Washington can choose to continue their education with over 300 graduate degree programs offered at its three campuses. This university provides PhDs in physics, mathematics, and machine learning and big data.

PhD in Machine Learning and Big Data

The PhD in Machine Learning and Big Data program at University of Washington teaches students valuable machine learning methods and how to conduct data analysis of big data sets. It will help students build a strong foundation in machine learning and big data methodologies.

Students need to meet the coursework requirements, write a general examination, conduct research to write a dissertation, and meet the credit hour requirement of 90 credits. Courses included in this PhD are Foundational Machine Learning, Advanced Machine Learning, and Advanced Statistical Learning.

PhD in Machine Learning and Big Data Overview

• Program Length: Up to 10 years
• Acceptance Rate: 10.58%
• Tuition and Fees: $6,725/quarter (in state); $11,688/quarter (out of state)
• PhD Funding Opportunities: Fellowships, internships, and research assistantships

PhD in Machine Learning and Big Data Admission Requirements

• GRE scores (optional)
• Funding application

Can You Get a PhD in Machine Learning Online?

No, you cannot get a PhD in machine learning online. However, you can pursue an online PhD in computer science with a machine learning component such as an online machine learning course or specialization. Many fantastic online computer science PhDs will help you fine-tune your machine learning expertise.

Best Online PhD Programs in Machine Learning

How long does it take to get a phd in machine learning.

It takes between four and 10 years to get a PhD in Machine learning. According to Statista, the average time to complete a doctorate degree is seven and a half years. A PhD takes this long to complete because it is research-intensive and involves several stages.

Students need to take required courses and complete coursework in the first two years of a PhD program. Once the coursework is complete, students will write an examination to ensure they have completed all the essential skills and expertise in machine learning.

In the final years of a PhD, students conduct research and write a dissertation which takes between two to five years to finish. Usually, the school will have information on their website regarding the maximum time students have to meet all the PhD requirements.

Is a PhD in Machine Learning Hard?

Yes, a PhD in Machine Learning is hard because it is research-driven. If you decide to pursue a PhD in machine learning, you need to ensure that you are motivated and determined to work hard because this program involves many hours of independent research and writing.

A PhD is also a lengthy degree program that takes a minimum of four years to complete. Don’t let the difficulty of a PhD in machine learning discourage you, though. If you are determined and enjoy researching and learning about machine learning, you will succeed.

How Much Does It Cost to Get a PhD in Machine Learning?

It costs $19,314 annually to get a PhD in Machine Learning , according to the figures from 2019 stated by the National Center for Education Statistics (NCES). The total tuition of your machine learning PhD depends on specific factors, including format, location, school, and specialization.

Colleges and universities are usually public or private institutions. Depending on what kind of school you attend, the tuition will differ. The average tuition for a PhD at a public institution is $12,171, while a PhD at a private institution costs $25,929. Search your school’s website or contact it directly to learn about the specific tuition costs of your PhD program.

How to Pay for a PhD in Machine Learning: PhD Funding Options

The funding options that students can use to pay for their PhD in machine learning include research assistantships, teaching assistantships, fellowships, internships, grants, and stipends. These funding options will lighten the financial burden of pursuing a PhD in machine learning.

Some schools offer teaching assistantships to students. You work a certain number of hours per week and receive a stipend or a tuition waiver or discount. A research assistantship is similar to a teaching assistantship, but they have different duties. According to Statista, research assistantships are the most common funding option for doctoral degrees .

Find out directly from your school if there are available paid internships, along with any other funding opportunities for PhD students in machine learning. Some schools award funding opportunities to students nominated by faculty members.

Best Online Master’s Degrees

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What Is the Difference Between a Machine Learning Master’s Degree and PhD?

The difference between a machine learning master’s degree and a PhD is that a PhD is research-intensive and focused, while a master’s degree is more focused on one’s career and may or may not include research for a master’s thesis.

A PhD is the highest degree level that a person can pursue, whereas a master’s degree is one level below. According to Statista, PhD degree holders make more than master’s degree graduates . Upon completing a master’s degree, students can earn an average salary of $92,272, while PhD graduates earn an average salary of $136,702.

Master’s vs PhD in Machine Learning Job Outlook

You can get a job as a computer information research scientist with a master’s degree, which comes with a job outlook of 22 percent . This is much faster than the average job outlook. With a PhD in machine learning, you can get any job in machine learning, but a job that explicitly requires a PhD is a university lecturer.

The job outlook for a machine learning lecturer is 12 percent , according to information cited by the US Bureau of Labor Statistics (BLS). This job outlook is much lower than that of a computer information research scientist. However, 12 percent is still an excellent average growth rate.

Difference in Salary for Machine Learning Master’s vs PhD

There is a significant contrast in earnings between a Machine learning PhD and a Machine learning Master’s degree. Although PayScale does not list the salary of Machine learning graduates specifically, it lists salary information for artificial intelligence, a field closely related to machine learning.

The average salary of an artificial intelligence PhD graduate is $115,000, while an AI master’s degree graduate earns an average salary of $103,000, annually . As you can see, a PhD will get you a very high average annual wage, and your salary can increase depending on your experience, location, and position.

Related Machine Learning Degrees

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Why You Should Get a PhD in Machine Learning

You should get a PhD in machine learning because it will open up new job opportunities, help you earn a higher salary, and allow you to add value to the machine learning industry. If you enjoy doing research, learning new things, and want to earn a higher salary, then a PhD is perfect for you.

Reasons for Getting a PhD in Machine Learning

• Higher salaries. Earning a PhD ensures that you will get a job with a high-paying salary. A PhD is the highest degree level that you can achieve, and PhD graduates earn a significantly higher salary than associate, bachelor’s, or master’s degree holders.
• Contributing to your professional industry. While completing a PhD, students conduct a lot of original research, broaden their skills and add value to their field. At the end of a PhD, students submit a dissertation, a document that identifies a problem within the industry and presents a solution through research.
• Enhancing specialized and soft skills. A PhD will help you improve and gain valuable specialized skills and techniques in machine learning, such as statistics and natural language processing. You will also gain excellent soft skills in machine learning, like problem-solving and time management.
• Increasing job opportunities. Once you earn your PhD, your job opportunities will increase. A PhD will help you get a senior profession, such as a lecturer or senior machine learning engineer. According to PayScale, a senior machine learning engineer earns an annual wage of $153,255 .
• Gaining valuable knowledge. Due to a PhD’s research-intensive nature, students constantly learn new things and gain valuable knowledge. If you enjoy learning, you should get a PhD because the learning opportunities are endless.

Getting a PhD in Machine Learning: Machine Learning PhD Coursework

Getting a PhD in Machine Learning requires taking specific courses to meet the necessary credit hours to graduate from your PhD program. Required courses typically include machine learning, introduction to AI, and statistical learning. Machine learning PhD coursework will help you gain essential machine learning skills and knowledge.

During the machine learning course, students will learn about the fundamental topics and techniques in machine learning, such as logistic regression, clustering, classifications, deep neural networks, linear models, and support vector machines. This course encourages reinforcement learning by looking at several real-world examples.

Deep Learning

Deep learning is an essential part of machine learning and involves artificial neural networks. The deep learning course will teach students about theoretical and practical aspects of deep learning, including neural networks, optimization algorithms, and structured models.

Statistical Learning

This course will cover modern learning algorithms such as variational approximations, boosting, and support vector machines. While completing the statistical learning course, students will learn about statistical algorithms for data analysis and applications of signal processing. Students should know programming languages to enroll in this course.

Introduction to Artificial Intelligence

While completing a PhD in machine learning, students will need to complete an Artificial Intelligence course. An Introduction to AI course involves the study of models and theories related to systems that emulate human intelligence. Students will cover Bayesian networks, constraint satisfaction, probabilistic reasoning, and natural language processing.

Analysis of Algorithms

The analysis of algorithms course looks at different efficient algorithms and studies their complexity and correctness. Topics covered include network flow, dynamic programming, and amortized analysis. Students will discuss problems with no solutions and cover all different kinds of algorithms.

Best Master’s Degrees

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How to Get a PhD in Machine Learning: Doctoral Program Requirements

Read the list below to find out how to get a PhD in Machine Learning. There are specific criteria that each student needs to meet before being awarded their degree. Common requirements include the completion of coursework, a research project, and a final thesis.

A machine learning PhD usually requires 40 to 48 credit hours. Students must take about six core courses and one elective course. During the first four semesters of their programs, students need to complete a specific number of credits before the next stage of their PhD.

Research is a considerable part of a PhD, so most programs will require students to take one or more responsible conduct of research courses. The responsible conduct of research courses involves seminars and workshops that help students learn the best methods of conducting research. Some research courses involve a project that will help students learn through practice. 

Machine learning PhDs will include a research project after completing the required research courses. The research project will be directed by a faculty member and requires students to conduct research and write a report. Students will then present their reports to the PhD committee. Research projects usually focus on a specific topic within machine learning or computer science.

Once students have completed the core course requirements and written their research project, they must complete a qualifying exam which typically includes an oral examination. The PhD committee sets the qualifying exam and is designed to assess whether students are ready to conduct independent research for their PhD thesis.

You need to act as a teaching assistant for two semesters in a machine learning course. This is a requirement that only some PhD programs have. The graduate chair and coordinator set the requirements of the teaching practicum.

The PhD thesis requires a few years of research around a specific topic in machine learning. Students research a particular topic, and then they need to present their findings to the PhD committee. The thesis also includes a defense of the dissertation. Usually, students need to submit a thesis draft to the committee for approval before defending it.

Potential Careers With a Machine Learning Degree

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PhD in Machine Learning Salary and Job Outlook

Machine learning PhD graduates earn a highly favorable salary because a PhD is the highest degree level someone can earn. As stated above, PayScale does not list the average salary of a machine learning PhD graduate, but it notes that the average salary of an AI PhD graduate is  $115,000. Artificial intelligence is a field very closely related to machine learning.

The job outlook for a machine learning PhD graduate is between 12 and 22 percent. That is a very favorable job outlook. The BLS has stated that there are approximately 33,000 machine learning jobs each year.

What Can You Do With a PhD in Machine Learning?

With a PhD in machine learning, you can become a computer and information research scientist, a deep learning research engineer, or a computational linguist. Most higher education institutions offer career coaching services that help students prepare for interviews, write resumes, and find jobs. Contact your college to find out whether it offers career services.

Best Jobs with a PhD in Machine Learning

• Computer and Information Research Scientist
• Machine Learning Engineer
• Deep Learning Research Engineer
• Professor of Machine Learning
• Computational Linguist

What Is the Average Salary for a PhD in Machine Learning?

The average salary for a PhD in machine learning is $115,000 per year . This is a high average salary, but it varies based on factors such as experience, location, and job description. The more experience you have and the higher your degree level is, the higher your salary will be. If you decide to become a computer and information research scientist, you can earn an average salary of $131,490. If you are part of the 90th percentile, you can earn more than $208,000 annually .

Highest-Paying Machine Learning Jobs for PhD Grads

Best machine learning jobs with a doctorate.

Now that we have looked at all the details about a machine learning PhD and how to become a machine learning engineer , let’s look at the five highest-paying machine learning Jobs for PhD graduates, in detail.

A machine learning engineer develops artificial intelligence systems that research and create algorithms that use large datasets. These algorithms can learn and make accurate predictions. Machine learning engineers are very skilled at programming, and they use programming languages like Java and Python.

• Salary with a Machine Learning PhD: $112,513
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas, Massachusetts, Washington

Deep learning research engineers use deep learning platforms to create programming systems that copy brain functions. They do this using neural networks, which have a similar structure to the human brain. These programming systems are designed to learn without the help of humans.

• Salary with a Machine Learning PhD: $110,679

A computer and information research scientist improves and creates computer hardware and software using complex algorithms. They streamline these complex algorithms and enhance system efficiency. Computer and information research scientists' simplified algorithms lead to advancements in machine learning systems and other types of technology.

• Salary with a Machine Learning PhD: $100,384

Professors of machine learning usually teach students at a university or college. They will teach courses related to a specific field. In this case, they will teach courses related to machine learning. Professors at big institutions may also conduct research and experiments and publish original research. If you enjoy teaching you can become a professor of machine learning. 

• Salary with a Machine Learning PhD: $98,500
• Job Outlook: 12% job growth from 2020 to 2030
• Number of Jobs: 1,276,900
• Highest-Paying States: Alaska, New York, Utah, California, New Jersey

Computational linguists are a specific kind of computer scientist. They work with computers and teach computer systems how to understand human languages. They have excellent coding skills because they use programming languages to code. They also conduct computational linguistic research around a specific functional area or product line.

• Salary with a Machine Learning PhD: $80,330

Is a PhD in Machine Learning Worth It?

Yes, a PhD in Machine Learning is worth it. There are many excellent institutions that can help you earn a PhD in Machine Learning while providing valuable support from faculty members. Earning this type of degree can help you further your machine learning career.

If you pursue a PhD in machine learning, you will very likely add value to your industry with the research conducted during your dissertation. Completing a PhD takes many years and is research-intensive but completely worth it if you look at the jobs that use machine learning and the average PhD in Machine learning salary.

Additional Reading About Machine Learning

[query_class_embed] https://careerkarma.com/blog/machine-learning/ https://careerkarma.com/blog/online-machine-learning-courses/ https://careerkarma.com/blog/how-to-get-a-job-in-machine-learning/

PhD in Machine Learning FAQ

The cheapest PhD in machine learning is the PhD in Machine Learning and Data Science offered by University of California San Diego. The PhD in Machine Learning and Data Science tuition at University of California San Diego costs $11,442 per year for both residents and non-residents.

Many top companies hire machine learning PhD graduates, including Google, Microsoft, Adobe, PayPal, Amazon, IBM, and Duolingo. With a PhD in machine learning, you can land a job at one of these companies and earn a high salary.

Yes, there are many remote jobs available for machine learning graduates. A quick search on websites such as Indeed, Glassdoor, and LinkedIn can put you in touch with many possible machine learning jobs. Make sure you read the details of each job carefully before you apply.

Yes, you can get a job in machine learning with a bootcamp. Bootcamps are short, but they are  intensive and can teach you all the necessary skills to have a successful career in the machine learning industry. There are many excellent machine learning bootcamps to help you start your machine learning career.

About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication .

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DiscoverDataScience.org

PhD in Data Science – Your Guide to Choosing a Doctorate Degree Program

Created by aasif.faizal

Professional opportunities in data science are growing incredibly fast. That’s great news for students looking to pursue a career as a data scientist. But it also means that there are a lot more options out there to investigate and understand before developing the best educational path for you.

A PhD is the most advanced data science degree you can get, reflecting a depth of knowledge and technical expertise that will put you at the top of your field.

This means that PhD programs are the most time-intensive degree option out there, typically requiring that students complete dissertations involving rigorous research. This means that PhDs are not for everyone. Indeed, many who work in the world of big data hold master’s degrees rather than PhDs, which tend to involve the same coursework as PhD programs without a dissertation component. However, for the right candidate, a PhD program is the perfect choice to become a true expert on your area of focus.

If you’ve concluded that a data science PhD is the right path for you, this guide is intended to help you choose the best program to suit your needs. It will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts (like course load and tuition costs) that are part of the data science PhD decision-making process.

Data Science PhD vs. Masters: Choosing the right option for you

If you’re considering pursuing a data science PhD, it’s worth knowing that such an advanced degree isn’t strictly necessary in order to get good work opportunities. Many who work in the field of big data only hold master’s degrees, which is the level of education expected to be a competitive candidate for data science positions.

So why pursue a data science PhD?

Simply put, a PhD in data science will leave you qualified to enter the big data industry at a high level from the outset.

You’ll be eligible for advanced positions within companies, holding greater responsibilities, keeping more direct communication with leadership, and having more influence on important data-driven decisions. You’re also likely to receive greater compensation to match your rank.

However, PhDs are not for everyone. Dissertations require a great deal of time and an interest in intensive research. If you are eager to jumpstart a career quickly, a master’s program will give you the preparation you need to hit the ground running. PhDs are appropriate for those who want to commit their time and effort to schooling as a long-term investment in their professional trajectory.

For more information on the difference between data science PhD’s and master’s programs, take a look at our guide here.

Topics include:

• Can I get an Online Ph.D in Data Science?
• Overview of Ph.d Coursework

Preparing for a Doctorate Program

Building a solid track record of professional experience, things to consider when choosing a school.

• What Does it Cost to Get a Ph.D in Data Science?
• School Listings

Data Science PhD Programs, Historically

Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry. But, PhD programs are heavily research oriented and require a somewhat long term investment of time, money, and energy to obtain. The issue that some data science PhD holders are reporting, especially in industry settings, is that that the state of the art is moving so quickly, and that the data science industry is evolving so rapidly, that an abundance of research oriented expertise is not always what’s heavily sought after.

Instead, many companies are looking for candidates who are up to date with the latest data science techniques and technologies, and are willing to pivot to match emerging trends and practices.

One recent development that is making the data science graduate school decisions more complex is the introduction of specialty master’s degrees, that focus on rigorous but compact, professional training. Both students and companies are realizing the value of an intensive, more industry-focused degree that can provide sufficient enough training to manage complex projects and that are more client oriented, opposed to research oriented.

However, not all prospective data science PhD students are looking for jobs in industry. There are some pretty amazing research opportunities opening up across a variety of academic fields that are making use of new data collection and analysis tools. Experts that understand how to leverage data systems including statistics and computer science to analyze trends and build models will be in high demand.

Can You Get a PhD in Data Science Online?

While it is not common to get a data science Ph.D. online, there are currently two options for those looking to take advantage of the flexibility of an online program.

Indiana University Bloomington and Northcentral University both offer online Ph.D. programs with either a minor or specialization in data science.

Given the trend for schools to continue increasing online offerings, expect to see additional schools adding this option in the near future.

Overview of PhD Coursework

A PhD requires a lot of academic work, which generally requires between four and five years (sometimes longer) to complete.

Here are some of the high level factors to consider and evaluate when comparing data science graduate programs.

How many credits are required for a PhD in data science?

On average, it takes 71 credits to graduate with a PhD in data science — far longer (almost double) than traditional master’s degree programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and really great career preparation.

What’s the core curriculum like?

In a data science doctoral program, you’ll be expected to learn many skills and also how to apply them across domains and disciplines. Core curriculums will vary from program to program, but almost all will have a core foundation of statistics.

All PhD candidates will have to take a qualifying exam. This can vary from university to university, but to give you some insight, it is broken up into three phases at Yale. They have a practical exam, a theory exam and an oral exam. The goal is to make sure doctoral students are developing the appropriate level of expertise.

Dissertation

One of the final steps of a PhD program involves presenting original research findings in a formal document called a dissertation. These will provide background and context, as well as findings and analysis, and can contribute to the understanding and evolution of data science. A dissertation idea most often provides the framework for how a PhD candidate’s graduate school experience will unfold, so it’s important to be thoughtful and deliberate while considering research opportunities.

Since data science is such a rapidly evolving field and because choosing the right PhD program is such an important factor in developing a successful career path, there are some steps that prospective doctoral students can take in advance to find the best-fitting opportunity.

Join professional associations

Even before being fully credentials, joining professional associations and organizations such as the Data Science Association and the American Association of Big Data Professionals is a good way to get exposure to the field. Many professional societies are welcoming to new members and even encourage student participation with things like discounted membership fees and awards and contest categories for student researchers. One of the biggest advantages to joining is that these professional associations bring together other data scientists for conference events, research-sharing opportunities, networking and continuing education opportunities.

Leverage your social network

Be on the lookout to make professional connections with professors, peers, and members of industry. There are a number of LinkedIn groups dedicated to data science. A well-maintained professional network is always useful to have when looking for advice or letters of recommendation while applying to graduate school and then later while applying for jobs and other career-related opportunities.

Kaggle competitions

Kaggle competitions provide the opportunity to solve real-world data science problems and win prizes. A list of data science problems can be found at Kaggle.com . Winning one of these competitions is a good way to demonstrate professional interest and experience.

Internships

Internships are a great way to get real-world experience in data science while also getting to work for top names in the world of business. For example, IBM offers a data science internship which would also help to stand out when applying for PhD programs, as well as in seeking employment in the future.

Demonstrating professional experience is not only important when looking for jobs, but it can also help while applying for graduate school. There are a number of ways for prospective students to gain exposure to the field and explore different facets of data science careers.

Get certified

There are a number of data-related certificate programs that are open to people with a variety of academic and professional experience. DeZyre has an excellent guide to different certifications, some of which might help provide good background for graduate school applications.

Conferences

Conferences are a great place to meet people presenting new and exciting research in the data science field and bounce ideas off of newfound connections. Like professional societies and organizations, discounted student rates are available to encourage student participation. In addition, some conferences will waive fees if you are presenting a poster or research at the conference, which is an extra incentive to present.

It can be hard to quantify what makes a good-fit when it comes to data science graduate school programs. There are easy to evaluate factors, such as cost and location, and then there are harder to evaluate criteria such as networking opportunities, accessibility to professors, and the up-to-dateness of the program’s curriculum.

Nevertheless, there are some key relevant considerations when applying to almost any data science graduate program.

What most schools will require when applying:

• All undergraduate and graduate transcripts
• A statement of intent for the program (reason for applying and future plans)
• Letters of reference
• Application fee
• Online application
• A curriculum vitae (outlining all of your academic and professional accomplishments)

What Does it Cost to Get a PhD in Data Science?

The great news is that many PhD data science programs are supported by fellowships and stipends. Some are completely funded, meaning the school will pay tuition and basic living expenses. Here are several examples of fully funded programs:

• University of Southern California
• University of Nevada, Reno
• Kennesaw State University
• Worcester Polytechnic Institute
• University of Maryland

For all other programs, the average range of tuition, depending on the school can range anywhere from $1,300 per credit hour to $2,000 amount per credit hour. Remember, typical PhD programs in data science are between 60 and 75 credit hours, meaning you could spend up to $150,000 over several years.

That’s why the financial aspects are so important to evaluate when assessing PhD programs, because some schools offer full stipends so that you are able to attend without having to find supplemental scholarships or tuition assistance.

Can I become a professor of data science with a PhD.? Yes! If you are interested in teaching at the college or graduate level, a PhD is the degree needed to establish the full expertise expected to be a professor. Some data scientists who hold PhDs start by entering the field of big data and pivot over to teaching after gaining a significant amount of work experience. If you’re driven to teach others or to pursue advanced research in data science, a PhD is the right degree for you.

Do I need a master’s in order to pursue a PhD.? No. Many who pursue PhDs in Data Science do not already hold advanced degrees, and many PhD programs include all the coursework of a master’s program in the first two years of school. For many students, this is the most time-effective option, allowing you to complete your education in a single pass rather than interrupting your studies after your master’s program.

Can I choose to pursue a PhD after already receiving my master’s? Yes. A master’s program can be an opportunity to get the lay of the land and determine the specific career path you’d like to forge in the world of big data. Some schools may allow you to simply extend your academic timeline after receiving your master’s degree, and it is also possible to return to school to receive a PhD if you have been working in the field for some time.

If a PhD. isn’t necessary, is it a waste of time? While not all students are candidates for PhDs, for the right students – who are keen on doing in-depth research, have the time to devote to many years of school, and potentially have an interest in continuing to work in academia – a PhD is a great choice. For more information on this question, take a look at our article Is a Data Science PhD. Worth It?

Complete List of Data Science PhD Programs

Below you will find the most comprehensive list of schools offering a doctorate in data science. Each school listing contains a link to the program specific page, GRE or a master’s degree requirements, and a link to a page with detailed course information.

Note that the listing only contains true data science programs. Other similar programs are often lumped together on other sites, but we have chosen to list programs such as data analytics and business intelligence on a separate section of the website.

Boise State University  – Boise, Idaho PhD in Computing – Data Science Concentration

The Data Science emphasis focuses on the development of mathematical and statistical algorithms, software, and computing systems to extract knowledge or insights from data.  

In 60 credits, students complete an Introduction to Graduate Studies, 12 credits of core courses, 6 credits of data science elective courses, 10 credits of other elective courses, a Doctoral Comprehensive Examination worth 1 credit, and a 30-credit dissertation.

Electives can be taken in focus areas such as Anthropology, Biometry, Ecology/Evolution and Behavior, Econometrics, Electrical Engineering, Earth Dynamics and Informatics, Geoscience, Geostatistics, Hydrology and Hydrogeology, Materials Science, and Transportation Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $7,236 total (Resident), $24,573 total (Non-resident)

View Course Offerings

Bowling Green State University  – Bowling Green, Ohio Ph.D. in Data Science

Data Science students at Bowling Green intertwine knowledge of computer science with statistics.

Students learn techniques in analyzing structured, unstructured, and dynamic datasets.

Courses train students to understand the principles of analytic methods and articulating the strengths and limitations of analytical methods.

The program requires 60 credit hours in the studies of Computer Science (6 credit hours), Statistics (6 credit hours), Data Science Exploration and Communication, Ethical Issues, Advanced Data Mining, and Applied Data Science Experience.

Students must also complete 21 credit hours of elective courses, a qualifying exam, a preliminary exam, and a dissertation.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,418 (Resident), $14,410 (Non-resident)

Brown University  – Providence, Rhode Island PhD in Computer Science – Concentration in Data Science

Brown University’s database group is a world leader in systems-oriented database research; they seek PhD candidates with strong system-building skills who are interested in researching TupleWare, MLbase, MDCC, Crowd DB, or PIQL.

In order to gain entrance, applicants should consider first doing a research internship at Brown with this group. Other ways to boost an application are to take and do well at massive open online courses, do an internship at a large company, and get involved in a large open-source software project.

Coding well in C++ is preferred.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $62,680 total

Chapman University  – Irvine, California Doctorate in Computational and Data Sciences

Candidates for the doctorate in computational and data science at Chapman University begin by completing 13 core credits in basic methodologies and techniques of computational science.

Students complete 45 credits of electives, which are personalized to match the specific interests and research topics of the student.

Finally, students complete up to 12 credits in dissertation research.

Applicants must have completed courses in differential equations, data structures, and probability and statistics, or take specific foundation courses, before beginning coursework toward the PhD.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,538 per year

Clemson University / Medical University of South Carolina (MUSC) – Joint Program – Clemson, South Carolina & Charleston, South Carolina Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

The PhD in biomedical data science and informatics is a joint program co-authored by Clemson University and the Medical University of South Carolina (MUSC).

Students choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students complete 65-68 credit hours, and take courses in each of 5 areas: biomedical informatics foundations and applications; computing/math/statistics/engineering; population health, health systems, and policy; biomedical/medical domain; and lab rotations, seminars, and doctoral research.

Applicants must have a bachelor’s in health science, computing, mathematics, statistics, engineering, or a related field, and it is recommended to also have competency in a second of these areas.

Program requirements include a year of calculus and college biology, as well as experience in computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,858 total (South Carolina Resident), $22,566 total (Non-resident)

View Course Offerings – Clemson

George Mason University  – Fairfax, Virginia Doctor of Philosophy in Computational Sciences and Informatics – Emphasis in Data Science

George Mason’s PhD in computational sciences and informatics requires a minimum of 72 credit hours, though this can be reduced if a student has already completed a master’s. 48 credits are toward graduate coursework, and an additional 24 are for dissertation research.

Students choose an area of emphasis—either computer modeling and simulation or data science—and completed 18 credits of the coursework in this area. Students are expected to completed the coursework in 4-5 years.

Applicants to this program must have a bachelor’s degree in a natural science, mathematics, engineering, or computer science, and must have knowledge and experience with differential equations and computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $13,426 total (Virginia Resident), $35,377 total (Non-resident)

Harrisburg University of Science and Technology  – Harrisburg, Pennsylvania Doctor of Philosophy in Data Sciences

Harrisburg University’s PhD in data science is a 4-5 year program, the first 2 of which make up the Harrisburg master’s in analytics.

Beyond this, PhD candidates complete six milestones to obtain the degree, including 18 semester hours in doctoral-level courses, such as multivariate data analysis, graph theory, machine learning.

Following the completion of ANLY 760 Doctoral Research Seminar, students in the program complete their 12 hours of dissertation research bringing the total program hours to 36.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $14,940 total

Icahn School of Medicine at Mount Sinai  – New York, New York Genetics and Data Science, PhD

As part of the Biomedical Science PhD program, the Genetics and Data Science multidisciplinary training offers research opportunities that expand on genetic research and modern genomics. The training also integrates several disciplines of biomedical sciences with machine learning, network modeling, and big data analysis.

Students in the Genetics and Data Science program complete a predetermined course schedule with a total of 64 credits and 3 years of study.

Additional course requirements and electives include laboratory rotations, a thesis proposal exam and thesis defense, Computer Systems, Intro to Algorithms, Machine Learning for Biomedical Data Science, Translational Genomics, and Practical Analysis of a Personal Genome.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $31,303 total

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana PhD in Data Science PhD Minor in Applied Data Science

Doctoral candidates pursuing the PhD in data science at Indiana University-Purdue must display competency in research, data analytics, and at management and infrastructure to earn the degree.

The PhD is comprised of 24 credits of a data science core, 18 credits of methods courses, 18 credits of a specialization, written and oral qualifying exams, and 30 credits of dissertation research. All requirements must be completed within 7 years.

Applicants are generally expected to have a master’s in social science, health, data science, or computer science. 

Currently a majority of the PhD students at IUPUI are funded by faculty grants and two are funded by the federal government. None of the students are self funded.

IUPUI also offers a PhD Minor in Applied Data Science that is 12-18 credits. The minor is open to students enrolled at IUPUI or IU Bloomington in a doctoral program other than Data Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $9,228 per year (Indiana Resident), $25,368 per year (Non-resident)

Jackson State University – Jackson, Mississippi PhD Computational and Data-Enabled Science and Engineering

Jackson State University offers a PhD in computational and data-enabled science and engineering with 5 concentration areas: computational biology and bioinformatics, computational science and engineering, computational physical science, computation public health, and computational mathematics and social science.

Students complete 12 credits of common core courses, 12 credits in the specialization, 24 credits of electives, and 24 credits in dissertation research.

Students may complete the doctoral program in as little as 5 years and no more than 8 years.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,270 total

Kennesaw State University  – Kennesaw, Georgia PhD in Analytics and Data Science

Students pursuing a PhD in analytics and data science at Kennesaw State University must complete 78 credit hours: 48 course hours and 6 electives (spread over 4 years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship.

Prior to dissertation research, the comprehensive examination will cover material from the three areas of study: computer science, mathematics, and statistics.

Successful applicants will have a master’s degree in a computational field, calculus I and II, programming experience, modeling experience, and are encouraged to have a base SAS certification.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,328 total (Georgia Resident), $19,188 total (Non-resident)

New Jersey Institute of Technology  – Newark, New Jersey PhD in Business Data Science

Students may enter the PhD program in business data science at the New Jersey Institute of Technology with either a relevant bachelor’s or master’s degree. Students with bachelor’s degrees begin with 36 credits of advanced courses, and those with master’s take 18 credits before moving on to credits in dissertation research.

Core courses include business research methods, data mining and analysis, data management system design, statistical computing with SAS and R, and regression analysis.

Students take qualifying examinations at the end of years 1 and 2, and must defend their dissertations successfully by the end of year 6.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $21,932 total (New Jersey Resident), $32,426 total (Non-resident)

New York University  – New York, New York PhD in Data Science

Doctoral candidates in data science at New York University must complete 72 credit hours, pass a comprehensive and qualifying exam, and defend a dissertation with 10 years of entering the program.

Required courses include an introduction to data science, probability and statistics for data science, machine learning and computational statistics, big data, and inference and representation.

Applicants must have an undergraduate or master’s degree in fields such as mathematics, statistics, computer science, engineering, or other scientific disciplines. Experience with calculus, probability, statistics, and computer programming is also required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,332 per year

View Course Offering

Northcentral University  – San Diego, California PhD in Data Science-TIM

Northcentral University offers a PhD in technology and innovation management with a specialization in data science.

The program requires 60 credit hours, including 6-7 core courses, 3 in research, a PhD portfolio, and 4 dissertation courses.

The data science specialization requires 6 courses: data mining, knowledge management, quantitative methods for data analytics and business intelligence, data visualization, predicting the future, and big data integration.

Applicants must have a master’s already.

Delivery Method: Online GRE: Required 2022-2023 Tuition: $16,794 total

Stevens Institute of Technology – Hoboken, New Jersey Ph.D. in Data Science

Stevens Institute of Technology has developed a data science Ph.D. program geared to help graduates become innovators in the space.

The rigorous curriculum emphasizes mathematical and statistical modeling, machine learning, computational systems and data management.

The program is directed by Dr. Ted Stohr, a recognized thought leader in the information systems, operations and business process management arenas.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $39,408 per year

University at Buffalo – Buffalo, New York PhD Computational and Data-Enabled Science and Engineering

The curriculum for the University of Buffalo’s PhD in computational and data-enabled science and engineering centers around three areas: data science, applied mathematics and numerical methods, and high performance and data intensive computing. 9 credit course of courses must be completed in each of these three areas. Altogether, the program consists of 72 credit hours, and should be completed in 4-5 years. A master’s degree is required for admission; courses taken during the master’s may be able to count toward some of the core coursework requirements.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,310 per year (New York Resident), $23,100 per year (Non-resident)

University of Colorado Denver – Denver, Colorado PhD in Big Data Science and Engineering

The University of Colorado – Denver offers a unique program for those students who have already received admission to the computer science and information systems PhD program.

The Big Data Science and Engineering (BDSE) program is a PhD fellowship program that allows selected students to pursue research in the area of big data science and engineering. This new fellowship program was created to train more computer scientists in data science application fields such as health informatics, geosciences, precision and personalized medicine, business analytics, and smart cities and cybersecurity.

Students in the doctoral program must complete 30 credit hours of computer science classes beyond a master’s level, and 30 credit hours of dissertation research.

The BDSE fellowship requires students to have an advisor both in the core disciplines (either computer science or mathematics and statistics) as well as an advisor in the application discipline (medicine and public health, business, or geosciences).

In addition, the fellowship covers full stipend, tuition, and fees up to ~50k for BDSE fellows annually. Important eligibility requirements can be found here.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $55,260 total

University of Marylan d  – College Park, Maryland PhD in Information Studies

Data science is a potential research area for doctoral candidates in information studies at the University of Maryland – College Park. This includes big data, data analytics, and data mining.

Applicants for the PhD must have taken the following courses in undergraduate studies: programming languages, data structures, design and analysis of computer algorithms, calculus I and II, and linear algebra.

Students must complete 6 qualifying courses, 2 elective graduate courses, and at least 12 credit hours of dissertation research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $16,238 total (Maryland Resident), $35,388 total (Non-resident)

University of Massachusetts Boston  – Boston, Massachusetts PhD in Business Administration – Information Systems for Data Science Track

The University of Massachusetts – Boston offers a PhD in information systems for data science. As this is a business degree, students must complete coursework in their first two years with a focus on data for business; for example, taking courses such as business in context: markets, technologies, and societies.

Students must take and pass qualifying exams at the end of year 1, comprehensive exams at the end of year 2, and defend their theses at the end of year 4.

Those with a degree in statistics, economics, math, computer science, management sciences, information systems, and other related fields are especially encouraged, though a quantitative degree is not necessary.

Students accepted by the program are ordinarily offered full tuition credits and a stipend ($25,000 per year) to cover educational expenses and help defray living costs for up to three years of study.

During the first two years of coursework, they are assigned to a faculty member as a research assistant; for the third year students will be engaged in instructional activities. Funding for the fourth year is merit-based from a limited pool of program funds

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $18,894 total (in-state), $36,879 (out-of-state)

University of Nevada Reno – Reno, Nevada PhD in Statistics and Data Science

The University of Nevada – Reno’s doctoral program in statistics and data science is comprised of 72 credit hours to be completed over the course of 4-5 years. Coursework is all within the scope of statistics, with titles such as statistical theory, probability theory, linear models, multivariate analysis, statistical learning, statistical computing, time series analysis.

The completion of a Master’s degree in mathematics or statistics prior to enrollment in the doctoral program is strongly recommended, but not required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,814 total (in-state), $22,356 (out-of-state)

University of Southern California – Los Angles, California PhD in Data Sciences & Operations

USC Marshall School of Business offers a PhD in data sciences and operations to be completed in 5 years.

Students can choose either a track in operations management or in statistics. Both tracks require 4 courses in fall and spring of the first 2 years, as well as a research paper and courses during the summers. Year 3 is devoted to dissertation preparation and year 4 and/or 5 to dissertation defense.

A bachelor’s degree is necessary for application, but no field or further experience is required.

Students should complete 60 units of coursework. If the students are admitted with Advanced Standing (e.g., Master’s Degree in appropriate field), this requirement may be reduced to 40 credits.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $63,468 total

University of Tennessee-Knoxville  – Knoxville, Tennessee The Data Science and Engineering PhD

The data science and engineering PhD at the University of Tennessee – Knoxville requires 36 hours of coursework and 36 hours of dissertation research. For those entering with an MS degree, only 24 hours of course work is required.

The core curriculum includes work in statistics, machine learning, and scripting languages and is enhanced by 6 hours in courses that focus either on policy issues related to data, or technology entrepreneurship.

Students must also choose a knowledge specialization in one of these fields: health and biological sciences, advanced manufacturing, materials science, environmental and climate science, transportation science, national security, urban systems science, and advanced data science.

Applicants must have a bachelor’s or master’s degree in engineering or a scientific field. 

All students that are admitted will be supported by a research fellowship and tuition will be included.

Many students will perform research with scientists from Oak Ridge national lab, which is located about 30 minutes drive from campus.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,468 total (Tennessee Resident), $29,656 total (Non-resident)

University of Vermont – Burlington, Vermont Complex Systems and Data Science (CSDS), PhD

Through the College of Engineering and Mathematical Sciences, the Complex Systems and Data Science (CSDS) PhD program is pan-disciplinary and provides computational and theoretical training. Students may customize the program depending on their chosen area of focus.

Students in this program work in research groups across campus.

Core courses include Data Science, Principles of Complex Systems and Modeling Complex Systems. Elective courses include Machine Learning, Complex Networks, Evolutionary Computation, Human/Computer Interaction, and Data Mining.

The program requires at least 75 credits to graduate with approval by the student graduate studies committee.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $12,204 total (Vermont Resident), $30,960 total (Non-resident)

University of Washington Seattle Campus – Seattle, Washington PhD in Big Data and Data Science

The University of Washington’s PhD program in data science has 2 key goals: training of new data scientists and cyberinfrastructure development, i.e., development of open-source tools and services that scientists around the world can use for big data analysis.

Students must take core courses in data management, machine learning, data visualization, and statistics.

Students are also required to complete at least one internship that covers practical work in big data.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $17,004 per year (Washington resident), $30,477 (non-resident)

University of Wisconsin-Madison – Madison, Wisconsin PhD in Biomedical Data Science

The PhD program in Biomedical Data Science offered by the Department of Biostatistics and Medical Informatics at UW-Madison is unique, in blending the best of statistics and computer science, biostatistics and biomedical informatics. 

Students complete three year-long course sequences in biostatistics theory and methods, computer science/informatics, and a specialized sequence to fit their interests.

Students also complete three research rotations within their first two years in the program, to both expand their breadth of knowledge and assist in identifying a research advisor.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,728 total (in-state), $24,054 total (out-of-state)

Vanderbilt University – Nashville, Tennessee Data Science Track of the BMI PhD Program

The PhD in biomedical informatics at Vanderbilt has the option of a data science track.

Students complete courses in the areas of biomedical informatics (3 courses), computer science (4 courses), statistical methods (4 courses), and biomedical science (2 courses). Students are expected to complete core courses and defend their dissertations within 5 years of beginning the program.

Applicants must have a bachelor’s degree in computer science, engineering, biology, biochemistry, nursing, mathematics, statistics, physics, information management, or some other health-related field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $53,160 per year

Washington University in St. Louis – St. Louis, Missouri Doctorate in Computational & Data Sciences

Washington University now offers an interdisciplinary Ph.D. in Computational & Data Sciences where students can choose from one of four tracks (Computational Methodologies, Political Science, Psychological & Brain Sciences, or Social Work & Public Health).

Students are fully funded and will receive a stipend for at least five years contingent on making sufficient progress in the program.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $59,420 total

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

The PhD in data science at Worcester Polytechnic Institute focuses on 5 areas: integrative data science, business intelligence and case studies, data access and management, data analytics and mining, and mathematical analysis.

Students first complete a master’s in data science, and then complete 60 credit hours beyond the master’s, including 30 credit hours of research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $28,980 per year

Yale University – New Haven, Connecticut PhD Program – Department of Stats and Data Science

The PhD in statistics and data science at Yale University offers broad training in the areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. Students complete 12 courses in the first year in these topics.

Students are required to teach one course each semester of their third and fourth years.

Most students complete and defend their dissertations in their fifth year.

Applicants should have an educational background in statistics, with an undergraduate major in statistics, mathematics, computer science, or similar field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $46,900 total

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GRADUATE RESEARCH

Machine Learning & Data Science

Doctoral student research in machine learning & data science.

Machine learning is the sub-field of AI concerned with learning models from data. Once learned, these models can be use to label future data, make predictions about the future, aid in decision making, or explain the past. At Rice, we cover multiple sub-fields of machine learning, including deep learning.

Faculty members leading research in Machine Learning & Data Science are as follows:

• Maryam Aliakbarpour 
• Vladimir Braverman
• Hanjie Chen
• Xia (Ben) Hu
• Chris Jermaine
• Tasos Kyrillidis
• Vicente Ordóñez-Román
• Anshumali Shrivastava
• Arlei Silva
• Devika Subramanian
• Vaibhav Unhelkar

The videos below represent current student research projects. 

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Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding intellectual skills who will carry forward productive research on the complex organizational, financial, and technological issues that characterize an increasingly competitive and challenging business world.

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Click here for answers to many of the most frequently asked questions.

PhD studies at MIT Sloan are intense and individual in nature, demanding a great deal of time, initiative, and discipline from every candidate. But the rewards of such rigor are tremendous:  MIT Sloan PhD graduates go on to teach and conduct research at the world's most prestigious universities.

PhD Program curriculum at MIT Sloan is organized under the following three academic areas: Behavior & Policy Sciences; Economics, Finance & Accounting; and Management Science. Our nine research groups correspond with one of the academic areas, as noted below.

MIT Sloan PhD Research Groups

Behavioral & policy sciences.

Economic Sociology

Institute for Work & Employment Research

Organization Studies

Technological Innovation, Entrepreneurship & Strategic Management

Economics, Finance & Accounting

Accounting  

Management Science

Information Technology

System Dynamics  

Those interested in a PhD in Operations Research should visit the Operations Research Center .  

PhD Program Structure

Additional information including coursework and thesis requirements.

MIT Sloan Predoctoral Opportunities

MIT Sloan is eager to provide a diverse group of talented students with early-career exposure to research techniques as well as support in considering research career paths.

Rising Scholars Conference

The fourth annual Rising Scholars Conference on October 25 and 26 gathers diverse PhD students from across the country to present their research.

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The goal of the MIT Sloan PhD Program's admissions process is to select a small number of people who are most likely to successfully complete our rigorous and demanding program and then thrive in academic research careers. The admission selection process is highly competitive; we aim for a class size of nineteen students, admitted from a pool of hundreds of applicants.

What We Seek

• Outstanding intellectual ability
• Excellent academic records
• Previous work in disciplines related to the intended area of concentration
• Strong commitment to a career in research

MIT Sloan PhD Program Admissions Requirements Common Questions

Dates and Deadlines

Admissions for 2024 is closed. The next opportunity to apply will be for 2025 admission. The 2025 application will open in September 2024. 

More information on program requirements and application components

Students in good academic standing in our program receive a funding package that includes tuition, medical insurance, and a fellowship stipend and/or TA/RA salary. We also provide a new laptop computer and a conference travel/research budget.

Funding Information

Throughout the year, we organize events that give you a chance to learn more about the program and determine if a PhD in Management is right for you.

PhD Program Events

June phd program overview.

During this webinar, you will hear from the PhD Program team and have the chance to ask questions about the application and admissions process.

July PhD Program Overview

August phd program overview, september 12 phd program overview.

Complete PhD Admissions Event Calendar

Unlike formulaic approaches to training scholars, the PhD Program at MIT Sloan allows students to choose their own adventure and develop a unique scholarly identity. This can be daunting, but students are given a wide range of support along the way - most notably having access to world class faculty and coursework both at MIT and in the broader academic community around Boston.

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Profiles of our current students

MIT Sloan produces top-notch PhDs in management. Immersed in MIT Sloan's distinctive culture, upcoming graduates are poised to innovate in management research and education.

Academic Job Market

Doctoral candidates on the current academic market

Academic Placements

Graduates of the MIT Sloan PhD Program are researching and teaching at top schools around the world.

view recent placements 

MIT Sloan Experience

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The PhD Program is integral to the research of MIT Sloan's world-class faculty. With a reputation as risk-takers who are unafraid to embrace the unconventional, they are engaged in exciting disciplinary and interdisciplinary research that often includes PhD students as key team members.

Research centers across MIT Sloan and MIT provide a rich setting for collaboration and exploration. In addition to exposure to the faculty, PhD students also learn from one another in a creative, supportive research community.

Throughout MIT Sloan's history, our professors have devised theories and fields of study that have had a profound impact on management theory and practice.

From Douglas McGregor's Theory X/Theory Y distinction to Nobel-recognized breakthroughs in finance by Franco Modigliani and in option pricing by Robert Merton and Myron Scholes, MIT Sloan's faculty have been unmatched innovators.

This legacy of innovative thinking and dedication to research impacts every faculty member and filters down to the students who work beside them.

Faculty Links

• Accounting Faculty
• Economic Sociology Faculty
• Finance Faculty
• Information Technology Faculty
• Institute for Work and Employment Research (IWER) Faculty
• Marketing Faculty
• Organization Studies Faculty
• System Dynamics Faculty
• Technological Innovation, Entrepreneurship, and Strategic Management (TIES) Faculty

Student Research

“MIT Sloan PhD training is a transformative experience. The heart of the process is the student’s transition from being a consumer of knowledge to being a producer of knowledge. This involves learning to ask precise, tractable questions and addressing them with creativity and rigor. Hard work is required, but the reward is the incomparable exhilaration one feels from having solved a puzzle that had bedeviled the sharpest minds in the world!” -Ezra Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship

Sample Dissertation Abstracts - These sample Dissertation Abstracts provide examples of the work that our students have chosen to study while in the MIT Sloan PhD Program.

We believe that our doctoral program is the heart of MIT Sloan's research community and that it develops some of the best management researchers in the world. At our annual Doctoral Research Forum, we celebrate the great research that our doctoral students do, and the research community that supports that development process.

The videos of their presentations below showcase the work of our students and will give you insight into the topics they choose to research in the program.

Attention To Retention: The Informativeness of Insiders’ Decision to Retain Shares

2024 PhD Doctoral Research Forum Winner - Gabriel Voelcker

Watch more MIT Sloan PhD Program  Doctoral Forum Videos

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Georgia Institute of Technology

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PhD Program

The machine learning (ML) Ph.D. program is a collaborative venture between Georgia Tech's colleges of Computing, Engineering, and Sciences. Approximately 25-30 students enter the program each year through nine different academic units. 

ML@GT manages all operations and curricular requirements for the new Ph.D. Program, which include four core and five elective courses, a qualifying exam, and a doctoral dissertation defense .

See the curriculum overview for more information.

Students admitted into the ML Ph.D. program can be advised by any of our  participating ML Ph.D. Program faculty .

More information about admission to the ML Ph.D. program can be found here .

More information about the program itself, including details on operations and curriculum outlined in the ML Handbook, can be found in the current student resources.

ML@GT Ph.D. Faculty Advisory Committee

• Aerospace Engineering (AE): Evangelos Theodorou, [email protected]
• Biomedical Engineering (BME): May Wang, [email protected]
• Chemical and Biomolecular Engineering (ChBE): Martha Grover, [email protected]
• Computational Science and Engineering (CSE): Polo Chau, [email protected]
• Electrical Computer Engineering (ECE): David Anderson, [email protected]
• Computer Science (CS): Jacob Abernethy, prof.gatech.edu
• Industrial Systems Engineering (ISyE): Yao Xie, [email protected]
• Interactive Computing (IC): Zsolt Kira, [email protected]
• Mathematics (MATH): Vladimir Koltchinskii, [email protected]

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Georgia Institute of Technology North Avenue, Atlanta, GA 30332 Phone: 404-894-2000

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Machine Learning and Big Data PhD Track

Optional PhD Tracks:   Statistical Genetics ,  Statistics in the Social Sciences ,  Machine Learning and Big Data

About The UW Department of Statistics now offers a PhD track in the area of Machine Learning and Big Data. All incoming and current students are eligible to apply. The goal of the PhD track is to prepare students to tackle large data analysis tasks with the most advanced tools in existence today, while building a strong methodological foundation. Students in this track will have a multidisciplinary experience, taking courses across departments and interacting with faculty and graduate students from these departments. A similar PhD track is being offered in  Computer Science and Engineering  (CSE), and students from both of these tracks will interact significantly in the core courses.

More details about ML @ UW can be found  here  and  here .

For application details, click  here .

Program Requirements

• Statistics Core:  STAT 570 ,  STAT 581 ,  STAT 582
• ML/BD Core:
• (i) Foundational ML:  STAT 535 (ii) One advanced ML course:  STAT 538  or  STAT 548 (iii) One CSE course:  CSE 544  (Databases) or CSE 512 (Visualization) (iv) One MLBD related elective such as a course from the list below and Two electives from the general electives list:        * Advanced Statistical Learning ( STAT 538 )       * Machine Learning for Big Data ( STAT 548 )       * Graphical Models ( CSE 515 )       * Visualization (CSE 512)       * Databases ( CSE 544 )       * Convex Optimization ( EE 578 )
• All other statistics PhD requirements hold, except  STAT 571  may be used to satisfy the Applied Data Analysis Project.
• STAT 583 is not required.

Advanced Data Science Transcriptable Option A student in the MLBD track can, in addition, choose to enroll/satisfy the Advanced Data Science Option. To further expand students' education and create a campus-wide community, students will register for at least 4 quarters in the weekly  eScience Community Seminar . Satisfying this option means that the student will have "ADS" listed on their transcript.

• eScience ADSO

ML Lunch Series A lunchtime seminar on a topic related to machine learning is held nearly weekly on Tuesdays during term. Lunch is provided. Updates are posted  here .

ML Mailing List General announcements related to machine learning are made on the  ML mailing list .

Doctor of Philosophy (Ph.D.) Programs

Graduate Programs Office ICS 2 Building, Suite 200 (949) 824-5158 Office Hours & Contact Info

The ICS doctoral programs have experienced tremendous growth in recent years, buoyed by faculty growth in all ICS departments (Computer Science, Informatics, and Statistics).  The School currently enrolls a vibrant and diverse community of more than 400 doctoral students who work side-by-side with world-class faculty and post-doctoral researchers to shape the future through advances across a remarkable range of research areas , from artificial intelligence, data science, and software systems to accessibility, digital media, and health informatics.

Graduates of the ICS doctoral programs move on to research and teaching careers in academia, advanced research and development positions in industry, non-profits, and the government, or leading roles in technology startups. Their work has profound impact on academia, industry, and society, enhancing how we live, interact, and play through information and computation.

A complete list of the Ph.D. programs in ICS is given below.

Computational Science

Computer science, informatics, networked systems, software engineering, student spotlights, persistence pays off for ivan de oliveira nunes in pursuing his ph.d..

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Vanessa Klotzman Aims to Make an Impact

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Elizabeth Ankrah Explores Tech for ‘Care Journeys’

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Exploring the influence of machine learning on the architecting of intelligent systems.

Abstract: Architecting involves crafting the foundational structure of intricate software systems, aiming to engineer high-quality software through intentional design. Over time, many foundational methods and…

Statistics & Data Science

Dietrich college of humanities and social sciences, ph.d. programs, our ph.d. programs enable students to pursue a wide range of research opportunities, including constructing and implementing advanced methods of data analysis to address crucial cross-disciplinary questions, along with developing the fundamental theory that supports these methods..

Unique opportunities for our Ph.D. students include:

• We host four cross-disciplinary joint Ph.D. programs for students who want to specialize in machine learning , public policy , neuroscience , and the link between engineering and policy .
• Our faculty have deep involvement in a range of important, data-rich scientific collaborations, including in the areas of genetics, neuroscience, astronomy, and the social sciences. This allows students to have easy access to both the crucial questions in these fields, and to the data that can provide the answers.
• Students begin work on their Advanced Data Analysis Project in the second semester. This year-long, faculty/student collaboration, distinct from the thesis, provides an immediate intensive research experience.
• Carnegie Mellon is home to the first Machine Learning Department . Many of our faculty maintain joint appointments with this Department and they (and our students) have strong connections to this exciting and growing area of research.

The programs leading to the degree of   Doctor of Philosophy in Statistics   seek to strike a balance between theoretical and applied statistics. The Ph.D. program prepares students for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods. Four to five years are usually needed to complete all requirements for the Ph.D. degree.

These pages present the requirements for each of our Ph.D. programs.

The page   "Core Ph.D. Requirements"   lays out the requirements for all Ph.D. students, while each of the four joint programs are described under the Joint Ph.D. Degrees pages. Our Ph.D. students can also earn a   Master of Science in Statistics   as an intermediate step towards their ultimate goal.

Joint Ph.D. Programs

Statistics/machine learning, statistics/public policy, statistics/engineering and public policy, statistics/neural computation  .

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PhD Program

Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; decision theory; game theory; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

Apply online here .

Department of Statistics and Data Science

The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686

Phone: (215) 898-8222

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Machine Learning Career: Pros and Cons of Having a PhD

• September 25, 2021 at 4:30 pm November 28, 2022 at 12:02 pm

It is often said that data science jobs are for seasoned professionals, and many job ads still show a preference for a profile with a PhD, with years of experience. Yet, many corporate employers have been disillusioned about the value that a PhD brings to the company. Likewise, many professionals, especially among those who just completed a PhD and were offered their first job, find the work sometimes unrewarding.

A PhD may command a slightly higher salary initially, and may be required for a position in a research lab (whether private or government-operated). But for many positions, it may not bring an advantage. Corporate work can be mundane and fast-paced, and the search for perfect algorithms is discouraged, as it hurts ROI. In many companies, a solution close to 80% of perfection is good enough, and requires far less time than reaching 99% perfection, especially since the machine learning models employed are just an approximation of the reality. People with a PhD are not well prepared for that.

Here are some of the negative aspects.

• Even if you pay someone to write your PhD thesis (such services exist), you may spend several years of your life working on your PhD, possibly in a stressful environment, with low pay, delaying buying a home, or getting married. Meanwhile, you see your non-PhD friends ahead of you in their personal life. If you married when working on your PhD, this could eliminate some of these problems.
• Some recruiters may say that you are over-qualified, that your experience is not really relevant to the job you are applying for (or too specialized), and that adapting to a fast-paced corporate environment might be challenging.
• If you land a job in the corporate world, you might find it menial or boring. You could be disappointed that the research you did during your PhD years is a thing of the past, not leading to anything else. This is especially true if your hope was to get a tenured position in the academia, but can’t get one despite your very strong credentials, due to the fierce competition. It can bring long-lasting regrets and nostalgia.
• You may be lacking some coding skills (SQL in particular), which put you at a disadvantage against a candidate with an applied master. Of course, it is always possible and desirable to gain these skills on your own (or via data camps) when working on your PhD.
• Your salary might not be higher than that of a younger candidate with a master degree and the right experience. Your cumulative wealth over your lifetime may be lower.
• Some employers (Google, Facebook, Microsoft, Wall Street,  or defense-related companies) routinely hire PhD’s to work on truly exciting projects. Some only hire from top universities and if your PhD was not from an ivy-league,  you will be by-passed. That said, there are plenty of companies that will hire non ivy-league candidates, and I think that’s a smart move. After all, I earned my PhD in some unknown university, and eventually succeeded in the corporate world.

For some, the pros outweigh the cons by a long shot. This was my case. I provide a few examples below.

• If your PhD was very applied in a hot field (in my case in 1993, processing digital satellite images for pattern detection), you learned how to code, played with a lot of messy data, and even got part-time job in the corporate world, related to your thesis when working on it, then you are up to a good start. In my case, solid funding for the research, and even data sets, came from governmental agencies (EU and others) and private companies (Total, for instance) trying to solve real problems. This adds credibility to your PhD experience. On the downside, my mentor was not a great scholar, but a good salesman able to attract many well paid contracts.
• If you earned your PhD abroad like I did, it is quite possible that you were paid better than your peers in US. In my case, my salary, as a teaching assistant, was similar to that of a high school teacher. And conference attendance (worldwide) was paid by the university or by the agencies that invited me as a speaker. Coming from abroad is sometimes perceived as an advantage, due to showing cultural adaptation, and in most cases, being multilingual and able to easily relocate in various locations if corporate needs ask for it.
• You can still continue to do your research, decades after leaving academia. I still write papers and books to this day. The level is even higher than during my PhD years, but the style and audience is very different, as I try to present advanced results, written in simple English, to a much larger audience. I find this more rewarding than publishing in scientific journals, read by very few, and obfuscated in jargon.
• There are great positions in many research labs, private or government, available only to PhD applicants. The salary can be very competitive.
• VC funding is usually contingent to having a well-known PhD scientist on staff, for startup companies. So if you create your own startup, or work for one, a PhD is definitely an advantage. Even when I started my own, self-funded publishing / media company (acquired by Tech Target in 2020, and focusing on machine learning), my wife keeps reminding me that I would have had considerably less success without my education, even though you don’t legally need any degree or license to operate this kind of business.

Conclusions

Having a PhD can definitely offer a strong advantage. It depends on the subject of your thesis, where you earned your PhD, and if you worked on real-life problems relevant to the business world. More theoretical PhD’s can still find attractive jobs in various research labs, private or government. The experience may be more rewarding, and probably less political, than a tenured position in academia. It goes both ways: it is not unusual for someone with a pure corporate / business background, to make a late career move to academia, sometimes in a business-related department. Or combining both: academia and corporate positions at the same time.

I wrote an article in 2018, about how to improve PhD programs to allow for an easy  transition to the business world. I called it a doctorship program, and you can read about it  here . I will conclude by saying that another PhD scientist, who earned his PhD in the same unknown math department as me at the same time (in Belgium), ended up becoming an executive at Yahoo, after a short stint (post-doc) at the MIT, working on transportation problems. His name is Didier Burton. Another one (Michel Bierlaire), same year, same math department, also with a short post-doc stint at MIT (mine was at Cambridge University), never got a corporate job, but he is now an happy full professor at EPFL. Also, a Data Science Central intern (reporting to me), originally from Cuba and with very strong academic credentials (PhD, Columbia University, EPFL) got his first corporate job after his internship with us (I strongly recommended him). Despite a mixed academic background in physics and biology, he is now chief data scientist of a private company. His name is Livan Alonso.

About the Author

Vincent Granville is a pioneering data scientist and machine learning expert, founder of  MLTechniques.com  and co-founder of  Data Science Central  (acquired by  TechTarget in 2020), former VC-funded executive, author and patent owner. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET, InfoSpace. Vincent is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS).

Vincent published in  Journal of Number Theory ,  Journal of the Royal Statistical Society  (Series B), and  IEEE Transactions on Pattern Analysis and Machine Intelligence . He is also the author of multiple books, available  here . He lives  in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math and probabilistic number theory.

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Research Assistant in AI for Quality Assurance in Manufacturing

School of Engineering at Jönköping University (JTH) is seeking an exceptional Research Assistant to join a cutting-edge project focused on developing innovative AI-based solutions for automated qua...

PhD Candidate, Detailed Video Understanding

Vacancy number 14860Job type PhD positionsHours (in fte) 1,0External/ internal ExternalLocation LeidenPlaced on 21 May 2024Closing date 22 June 2024 32 more days to applyThe Faculty of Science and the Leiden Institute of Advanced Computer Science ...

Scientific Assistant in Machine Learning for Healthcare

Scientific Assistant in Machine Learning for HealthcareThe Biomedical Data Science Lab (BMDS), headed by Prof. Catherine Jutzeler, is looking for a highly motivated and skilled Scientific Assistant...

Vacancy for two PhD researchers in Predictive and Prescriptive Process Modelling

You will work at the LIRIS research group of the Faculty of Economics and Business at KU Leuven. One position is hosted at KU Leuven, with an exchange period at University of Melbourne. The second ...

Postdoc in Machine Learning

Job descriptionWe are looking for a Postdoc in Machine Learning who will conduct research focussed on the modelling, prediction and planning of motions with applications to assistive technologies a...

Doctoral Researcher (Learning and Control for Autonomous Robotics)

Tampere University and Tampere University of Applied Sciences create a unique environment for multidisciplinary, inspirational and high-impact research and education. Our universities community has its competitive edges in technology, health and s...

PhD Candidate In geo artificial intelligence for planetary health

Job descriptionHelp us to further understand the interplay of global environmental change and people’s health by developing and applying cutting-edge geo-data engineering and geo-artificial intelli...

PhD: AI for data-driven representation optimization for time series

The work will be performed within the research division STADIUS at the Department of Electrical Engineering (ESAT). STADIUS' major research objective is to contribute to the development of improved...

Junior Professor Chair - Open position of Associate Professor (Maitre de conferences) in Virtual metallurgy for a sustainable industry of the future– Titularization in the Professors' corps at the end of a 3 to 6-year contract

Supporting establishment/organization: IMT - Mines Saint-EtienneName of School Director: Jacques FAYOLLESite concerned : Mines Saint-Etienne – Center for Materials and Structural ScienceProject name : Virtual metallurgy for a sustainable industry ...

PhD researcher Intelligent Wireless Networking on 6G RAN Optimization

Last application date Oct 30, 2024 00:00Department TW05 - Department of Information TechnologyContract Limited durationDegree Master in Computer Science, Mathematics, Artificial Intelligence, or equivalentOccupancy rate 100%Vacancy type Research s...

PhD Positions in Cancer Research

Are you looking for excellent research opportunities for your PhD studies at the forefront of cancer research? The German Cancer Research Center (DKFZ) in Heidelberg invites international students holding a Master’s degree in (molecular) biology, ...

Upcoming PhD Positions at the International Max Planck Research School for Molecules of Life in Autumn 2024

More information for the new call-out will come up during Autumn 2024. Stay tuned!The International Max Planck Research School for Molecules of Life (IMPRS-ML) will have an open call for fully-funded PhD student positions in the areas of biochemis...

PhD Researcher in Learning-based control algorithms for avatar animation in dyadic interactions

Last application date Dec 31, 2024 00:00Department TW05 - Department of Information TechnologyContract Limited durationDegree European master's degree in computer science, Artificial Intelligence, or equivalentOccupancy rate 100%Vacancy type Resea...

PhD Students

The CISPA Helmholtz Center for Information Security is looking for PhD Students in areas related to:Cybersecurity, Privacy and CryptographyMachine Learning and Data ScienceEfficient Algorithms and Foundations of Theoretical Computer ScienceSoftwar...

Research Associate (Postdoc) in machine learning optimization and FPGA acceleration for 5G/6G physical layer

The University of Luxembourg is seeking to hire a highly motivated and outstanding researcher in the area of machine learning optimization for physical layer applied to satellite communications in its Interdisciplinary Centre of Security and Trust...

Three-year PhD position in Development of interactive expert software stratifying the risk of lung cancer diagnosis in the setting of lung cancer screening based on mathematical modeling and machine learning approaches (funded by A*Midex)

RESEARCHER PROFILE: PhD/ R1: First stage Researcher             RESEARCH FIELD(S)1: Mathematics / Computer science / EngineeringMAIN SUB RESEARCH FIELD OR DISCIPLINES1: Medical sciences, Information sciences, Psychological sciencesJOB /OFFER DESCR...

Postdoc: Machine learning for imaging and metrology

The goal of the project is to develop new machine learning frameworks for high-resolution optical imaging and metrology. You will use the new methods to image complex wafer metrology targets and to...

Computational Postdoctoral Fellow - single-cell genomics in the Haniffa group

Do you want to help us improve human health and understand life on Earth? Make your mark by shaping the future to enable or deliver life-changing science to solve some of humanity’s greatest challenges.The Opportunity:The Cellular Genetics Program...

PhD position Surrogate modeling and Machine Learning for science and engineering

Last application date Jul 31, 2024 00:00Department TW05 - Department of Information TechnologyContract Limited durationDegree Master’s degree in Computer Science, Mathematics, Engineering, or equivalentOccupancy rate 100%Vacancy type Research staf...

PhD Candidate in Forming Effective Code Representations

(Valid from 25/11/2023 to 30/06/2024)   Language: English (UK) Country: Luxembourg   Organisation data: Interdisciplinary Centre of Security, Reliability and Trust Job Number: UOL06110 Contract Type: Fixed Term Contract Duration 36 Month Schedule ...

Research Associate (Postdoc) in Management Information Systems

SnT is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role in Luxembourg by fueling innovation through research partnerships with industry, boosting R&D ...

Postdoctoral researcher in Robotics

Reference number ORU 2.1.1-03228/2024We are looking for one postdoctoral researcher in robotics for a fixed-term position at the School of Science and Technology.Subject areaThe subject area for this position is Computer Science.BackgroundThe post...

PhD student Causal Machine Learning

Last application date Sep 01, 2024 00:00Department WE02 - Department of Applied Mathematics, Computer Science and StatisticsContract Limited durationDegree Master of Statistics, or Master of Mathematics, Physics, Computer Science, Engineering (eac...

Ph.D. in the Area of AI-Enabled Precision Health

Deadline: June 30th, 2024The University of Applied Sciences and Arts of Southern Switzerland (SUPSI) has opened a full time (100%) position for a PhD student in AI-enabled Precision Health at the Department of Innovative Technologies (DTI) located...

Postdoc VLSI Design by abutment for a monolithic 3D Integrated Technology

Job descriptionIn collaboration with IMEC, KIT, LIRMM, and EPFL, KTH is developing a design flow for implementing digital designs in an innovative and challenging monolithic 3D integration technolo...

(Postdoc offer) Statistical and Tensor Methods for Spatiotemporal Heterogeneous Data Analysis

Offer DescriptionWe are offering a postdoc position on the development of statistical and tensor decomposition methods for representation learning of heterogeneous data with application to the analysis neuroimaging data.Location: The CRAN laborato...

PhD Candidate: UX Design-Inspired Visual Cortical Prosthetics Development at the Donders Centre for Cognition

Employment 1.0 FTEGross monthly salary € 2,770 - € 3,539Required background Research University DegreeOrganizational unit Faculty of Social SciencesApplication deadline 11 June 2024The primary focus of this project is to refine our approach to dev...

PhD Student - Department of Information Technology

Last application date Aug 31, 2024 00:00Department TW05 - Department of Information TechnologyContract Limited durationDegree Master in computer science, Artificial Intelligence, or equivalent, with excellent ('honors'-level) gradesOccupancy rate ...

Integrated Communication and Sensing for Remote Healthcare

The Division WAVECORE is a multidisciplinary research group of the Department of Electrical Engineering (ESAT) focusing on a broad range of topic areas ranging from electromagnetic theory, microwav...

PhD position: Assessing and enhancing quality of time series data with AI methodologies

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Department of Astrophysics

Quicklinks und sprachwechsel, main navigation, phd programme in data science, graduate school for data science.

Data Science is a relatively young scientific discipline aimed at addressing important scientific and societal problems by focusing on obtaining, analyzing, and interpreting data. It has become an indispensable part of both academia and industry. Indeed, Data Science is now widely regarded as the fourth pillar of scientific discovery complementing theoretical, experimental, and computational approaches. 

The PhD Program in Data Science is aimed at preparing aspiring data science professionals for a successful career in academia or industry. To achieve this goal, enrolled students will engage in world-class research at one of the best Universities in Switzerland. They will also have access to a multidisciplinary curriculum and be able to collaborate with experienced faculty members from a large number of departments across the University.

Students have to be offered a PhD position by an individual faculty member of a participating research group. The faculty member together with the PhD candidate can then select an appropriate PhD program that best fits the academic profile of the candidate. The data science PhD program is tailored for students with a strong technical background in machine learning or data science, for example. Decisions regarding enrollment into the PhD program will be made by an admission committee.

Requirements

A Master's degree in Science, Mathematics, or Computer Science or an equivalent degree. Additional requirements may be attached to specific PhD openings. Further requirements may be listed in the regulations of the doctoral program.

Successful applicants have a strong scholastic record, a creative mindset, are able to communicate their work effectively, have a basic knowledge of programming and statistics, and, most of all, enjoy solving problems in Data Science.

In order to graduate, students need to fulfill the following requirements:

• Deposition and defense of a written dissertation about the student's independent research project
• Acquisition of 12 ECTS credits of advanced courses
• Additional requirements imposed by the admission committee, the doctoral committee, or the University

Curriculum 

The program includes a curricular part of at least 12 ECTS credits. Participating students can choose from a broad selection of research topics and classes in coordination with or as stipulated by their doctoral committee. The curricular part of the doctoral studies covers both the specific topic area of the research project and a general education in Data Science and data modeling in Natural Sciences. ECTS credits may also be awarded for active participation in conferences or other activities of relevance for the doctoral study.

Featured Courses

• Academia Industry Modeling (AIM) week
• During AIM week, teams of PhD students and post-doctoral fellows complete a one-week focused research project on applied problems proposed by industry partners. Industry representatives and participating faculty coordinate the formulation of the problem and supervise the research teams. Topics can cover all scientific interests and domains represented in the PhD program and in particular their interfaces.
• Modeling of Complex Systems In this hands-on course, the students learn how mathematical models can be used to describe and understand the dynamics of real-world systems in essentially all applied sciences, such as physics, biology, chemistry, engineering, economics, social sciences etc. The course covers concepts like integration methods, mechanistic and descriptive modeling, stochasticity, and Monte Carlo simulations. On the practical side, students will model and implement dynamical systems, understand the theoretical background and limitations, and practice understanding and communicating of models.

Official PhD regulations for the Data Science Programme:

Doctoral Program Regulations (PDF, 206 KB) Ordnung zum Doktoratsprogramm (PDF, 107 KB)

For PhDs starting after 1 June 2022 the updated regulations apply:

Ordnung zum Doktoratsprogramm (ab 2. Juni 2022) (PDF, 152 KB)

Doctoral Program Regulations (PDF, 93 KB)

Admissions to Promotion - Useful links and documents:

Admission to Doctoral Studies at the University of Zurich

Factsheet Admission to a PhD Program at the Department of Astrophysics (PDF, 621 KB)

Checklist for PhD students

"Confirmation of Supervision for Doctoral Students" (PDF, 242 KB)

"Acceptance Confirmation Structured Doctoral Program" (PDF, 225 KB)

Annual committee meeting minutes (PDF, 1 MB)

Confirmation Sheet - TA Hours (PDF, 224 KB)

Fact sheet PhD Defence (EN) / Merkblatt (DE)

Program Director Prof. Dr. Jan Dirk Wegner [email protected]

Doctoral Programme in Computer Science

The general theme of the DoCS doctoral programme is computer science. The focal areas of DoCS are Algorithmic bioinformatics; Algorithms, data analytics, and machine learning; Data Science; Networks and services; and Software systems.

Want to know more? Visit our profile & activities page to learn more about the programme.

Call for 10 fully-funded PhD positions at the graduate school of the 'Berlin Institute for the Foundations of Learning and Data'

For detailed information please refer to:

https://tub.stellenticket.de/de/offers/160622/?search%5Bfulltextsearch%5D=bifold&namespace=jobs_science&locale=en

The positions are part of the Graduate School. BIFOLD conducts scalable agile fundamental research in the field of Al, primarily in the areas of data management (DM) and machine learning (ML).

Based on the overarching research foci of BIFOLD, the Graduate School offers PhD projects in the areas of current challenges in Al, Data Science (DS) and distributed analysis of large amounts of data, with focus on DM, ML, and their intersection; including the development of novel theories, algorithms, and technologies, as well as prototypical systems and tools.

The topics within foundational research in DM and scalable data processing range from distributed DM to secure big data processing, knowledge and research data management, semantic technologies, data programming languages and their compilation, in particular automatic parallelization, distribution, and hardware adaptation as well as the processing of data in the Internet of Things. In this context, the focus lies in the development of novel systems and technologies. Topics within foundational research in ML include Bayesian inference, deep learning, reinforcement learning, and secure ML. Representative topics within the intersection of DM and ML include information integration, information visualization, and data cleansing, in terms of both individual steps and the holistic examination of (data) modelling in iterative and exploratory processes of Al applications in the sciences, medicine and humanities. Another relevant area is that of responsible Al, which aims to provide methods and technologies for Al applications to become understandable, reproducible, and compliant with ethical and legal frameworks.

Brief descriptions of the current research projects of each group and corresponding topics for PhD work can be found at https://www.bifold.berlin/education/thesis-opportunities . Closing date:  May 12, 2023

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PhD Position – New insights from existing PEM water electrolysis data: Optimizing data analysis using Machine Learning

machine learning -based software applications for materials science Develop code and utilize machine learning to support the automation of characterization and fabrication processes Ensure the integration

PhD student (m/f/d) | Developing and implementating of machine learning applications and data analysis in Translational Psychiatry

position as  PhD student (m/f/d) to develop and implemente machine learning applications and data analysis in Translational Psychiatry. Your group The MPIP Fellowship led by Prof. Dr. med. Falkai at the Max

PhD Position in Intelligent Infrastructure Systems, Machine Learning

in Computer Science or a related field Experience in software development Excellent programming skills Familiar with basics of machine learning Interest in mobility technology and/or simulations, with

FAIROmics - PhD fellowship in explainable machine learning techniques to support the design of plant-based fermented food products – Development of a serious game.

Job description:Title: DC13, PhD fellowship in explainable machine learning techniques to support the design of plant-based fermented food products – Development of a serious game to support the

Postdoc (f/m/d) / Scientific Assistant (f/m/d) Helmholtz AI Machine Learning Scientist / Completed university studies (Master/ PhD ) in the field of natural sciences, mathematics, earth and environment

Job description:Postdoc (f/m/d) / Scientific Assistant (f/m/d) Helmholtz AI Machine Learning Scientist With cutting-edge research in the fields of ENERGY, HEALTH and MATTER, around 1,500 employees from

568: DLR-DAAD Postdoctoral Fellowship Nr. 568: PhD physics, mathematics, material sciences or similar (f/m/x) - Optimized Thermoelectric Materials design by digital twinning and machine learning

4 Apr 2024 Job Information Organisation/Company Deutsches Zentrum für Luft - und Raumfahrt (DLR) Research Field Technology Engineering Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country Germany Application Deadline 7 Aug 2024 - 00:00 (UTC) Type of Contract To be...

PhD student (f/m/d) Chemoinformatics and machine learning

the complex food composition, which can lead to masking effects and molecular interactions in the food matrix. The applicant will work on the development of machine learning prediction models

FAIROmics - PhD fellowship in explainable machine learning techniques to support the design of plant-based fermented food products – Development of a serious game to support the design of plant-based

on knowledge representation and machine learning at scale. It is particularly interested in models with rich semantics, e.g., RDF knowledge graphs. You will be supervised by Prof. Dr. Axel Ngonga (head of group

Cambridge – Tübingen PhD Fellowships in Machine Learning

Ghahramani. Course web page . 4F10: Statistical Pattern Processing Taught by Mark Gales. Course web page . 4F13: Machine Learning Taught by Zoubin Ghahramani and Carl Rasmussen. Course web page . PREVIOUS

PhD position in the Machine Learning and Information Processing Group on DNA Data Storage Personal

08.09.2021, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13

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