phd in data science in switzerland

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Department of Astrophysics

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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]

Phd Degree in Data Science & Big Data, Switzerland

Explore the best universities for Phd degree in Data Science & Big Data in Switzerland, based on their reputation and research in the field.

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Data Science

(doctoral program), degree program: doctorate faculty of science.

General description Admission requirements ECTS credits Degree Language of instruction

General description

The doctoral program in Data Science includes a written dissertation about the own independent research project. The program further includes a curricular part of at least 12 ECTS credits. The curricular part is determined individually for each doctoral student by the doctoral committee and it covers both the specific topic area of the research project and a general education in Data Science and data analysis in Natural Sciences. ECTS credits may also be awarded for active participation in conferences or other activities of relevance for the doctoral study. The regular participation in weekly research seminars is compulsory (no ECTS credits). Additionally, doctoral students must take at least one teaching load in each semester.

Admission requirements

Admission to doctoral study is governed by the ordinance on admission to studies at the University of Zurich (Verordnung über die Zulassung an der Universität Zürich VZS). The main requirement for admission to doctoral study is a Master's degree from a university or an equivalent degree. Each PhD project must receive approval from a professor at the Faculty of Science or from a person who has the right to confer a PhD at the Faculty of Science. There is no general right to be granted admission to a PhD program. Admission may be made contingent on conditions and/or restrictions in the form of additional coursework. Conditions must be met prior to admission to doctoral study; restrictions may be fulfilled during doctoral study. Coursework required to meet conditions and/or restrictions may not exceed a total of 60 ECTS credits; requirements are specified according to the demands of the subject in which the PhD thesis will be written. The Vice Dean of Studies decides on admission, on recognition of similar degrees, and on conditions and/or additional requirements that may apply. As a rule, the language of instruction in the PhD programs is English. All applicants whose native language is not English, or whose prior studies have not been conducted in English, are required to provide proof of sufficient knowledge of English.

ECTS credits

12 ECTS Credits

Doctor of Philosophy (PhD)

Language of instruction

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Department of Informatics

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The Doctoral Program of Informatics is where academia and industry meet, where theories are developed and challenged in praxis, where new knowledge is created, and where scholars emerge. The Department of Informatics offers Switzerland’s best application oriented informatics and, internationally, ranks among the best institutions in its field. Application areas range from business information systems to social sciences, linguistics, natural sciences, and medicine. The Department creates joint research ventures and integrates their results in the teaching of students. In addition, internationally renowned visiting professors regularly give courses on special topics. All of us in the Department of Informatics wish you good fortune as you pursue your advanced degree, and we hope you will contact us if we can help you along the way.

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PhD Programme in Data Science

A Collaboration between ZHAW Zurich University of Applied Sciences and UZH University of Zurich

Application

Who can apply.

The aim of this programme is to give excellent master students with a degree in the fields of Science, Technology, Engineering and Mathematics (STEM), business management and economics the opportunity to apply for PhD work in Data Science, carried out at Zurich University of Applied Sciences ZHAW and with a PhD degree awarded by the University of Zurich UZH .

The program is open for students with a MSc degree from a university or a university of applied sciences .

PhD students with a MSc degree from a university of applied sciences (including degrees awarded by ZHAW such as Master of Science in Engineering (MSE) , MSc in Business Information Technology , MSc in Life Sciences with Specialization Applied Computational Life Sciences ) will have to fulfil the convergence process , consisting of attending additional modules at UZH during their PhD studies which are worth in total 30 ECTS .

Modus Operandi

The student will carry out the PhD research and has a co-supervisor at ZHAW . In addition, the student will be supervised by a Professor at UZH . The UZH supervisor must be associated with the Faculty of Science (MNF). The student will be enrolled as PhD student in the PhD programme in data science at UZH. The duration of the PhD is typically 4 years.

How to apply

The application consists of two steps :

In the first step an interested student has to find a co-supervisor at ZHAW. A research project has to be defined and a supervisor at UZH has to be identified. The student then applies to the ZHAW-UZH PhD Programme in Data Science

After acceptance in step one, the second step consists of formal admission as PhD student at UZH within the scope of the UZH PhD programme in data science

Identify co-supervisor at ZHAW, employment conditions, and research topic

Identify supervisor at UZH Faculty of Science (MNF)

Submit application documents (see below) to the scientific coordinator of the PhD programme: Frank-Peter Schilling ( [email protected] ).

The coordinator will formally approve the documents and send them to the commission

The application is evaluated by the commission of the ZHAW-UZH PhD programme, consisting of several representatives of ZHAW and UZH

Provided the outcome of step 5 is positive, the student and their co-supervisor at ZHAW will be accepted to the ZHAW-UZH PhD programme in Data Science

The student applies, with the recommendation of the commission, for enrolment in the UZH PhD programme in data science (see below)

Application Documents:

Application Form (PDF) (DOCX) (including funding information)

Appendices (required):

Project description (3-5 pages, including time plan) containing state of the art in the respective research field, relevant literature, and the scientific problem that will be tackled by the PhD work

CV of candidate (including publications, if any)

Master diploma (including grades)

CV of supervisor at ZHAW

PhD program at UZH

Accepted PhD candidates must also enrol in the PhD Programme in Data Science at University of Zurich UZH.

At UZH, the «Promotionsordnung für die Promotion an der Mathematisch-naturwissenschaftlichen Fakultät» applies.

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Phd programme, doing a phd at the zhaw school of engineering.

The ZHAW School of Engineering offers students with a Master’s degree the opportunity to do a doctorate in cooperation with its partner universities. While studying for your PhD, you work on applied research projects at the university of applied sciences and are supervised jointly by professors from the ZHAW and its partner university.

You thus benefit from the strengths of both types of university and, with your dissertation project, also contribute towards raising the profile of research at the School of Engineering. The School of Engineering, for its part, creates favourable conditions and prerequisites for supporting high-quality research.

As a doctoral student, you are enrolled in a regular PhD programme at a partner university and are required to go through its admission process. After completing the programme, you will receive a doctorate from the partner university in question. Most PhD networks are also open to students with a Master’s degree from a university of applied sciences. They are, however, required to complete the processes (specific to each faculty) that are necessary for admission to the partner university. In addition to formalised PhD programmes, it is also possible for individual agreements to be concluded between partner universities, doctoral students and ZHAW.

https://www.zhaw.ch/en/jobs/vacant-positions/

Formal PhD programmes with partner universities

ZHAW is part of the PhD Network Data Science . We thus participate in the University of Zurich’s PhD Programme in Data Science . A broad selection of topics is available for doctoral studies in data science and these explicitly include data-intensive areas of Industry 4.0, such as predictive maintenance, data analytics and risk management, and cloud computing.

ZHAW graduates with a Master of Science in Engineering (MSE) are also eligible for the joint doctoral programme run by ZHAW and the Università Ca' Foscari in Venice. They can complete their doctoral studies in data science over a period of four years. They spend one year in the Department of Environmental Sciences, Informatics and Statistics at the University of Venice and three years at the ZHAW School of Engineering. MSE graduates from ZHAW can participate directly in the programme without having to catch up on additional ECTS from the Master’s degree programme.

Important to know

The doctorate is always awarded by the partner university. It is therefore the regulations of the partner university that are decisive.
In most cases, doctoral students at ZHAW School of Engineering are employed as scientific assistants; the dissertation is normally embedded in a fairly large research project (Innosuisse, SNSF, Horizon Europe).
ZHAW School of Engineering supports dissertation projects that are related to one of its main research areas.
The dissertation topic is agreed on directly by the doctoral student and their supervising professors from the university of applied sciences and the partner university.
Doctoral students are expected to display a high degree of independence and discipline and to actively exchange ideas with the contact persons in the institutes and centres.

How will the ZHAW School of Engineering support me with my dissertation project?

Financial support through PhD programmes and internal calls
Time support, including for caregivers
Networking within the PhD community
Access to foreign universities and networks (e.g. Horizon Europe Doctoral Network, PhD networks)

ZHAW employees and students will find further information in the Intranet .

Interested? Then venture to take the next step

A dissertation project requires careful planning and preparation in both the professional and private environment. We will be happy to offer you a consultation.

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Department of Computer Science

Das in data science.

The DAS in Data Science provides a programme in continuous education that covers the field of data science, with an interdisciplinary viewpoint.

The DAS programme in Data Science offered by ETH Zurich focuses on the management, analysis and utilization of large and complex data sets. The interdisciplinary programme includes all levels of abstraction of technologies relevant to Data Science, from hardware and electronics, clusters and networks, through big data systems, to machine learning, algorithms and statistics. The participants learn how to understand and use complex data management (storage, querying, infrastructures, networks, etc.) and analysis techniques (machine learning, statistics, etc.) in order to utilise them in a broad range of applications. Given the broad range of research at ETH on data science related topics, the DAS in Data Science allows participants to choose from a diverse set of graduate courses. The programme is co-hosted by the Department of Computer Science ( D-INFK ), the Department of Information Technology and Electrical Engineering ( D-ITET ), and the Department of Mathematics ( D-MATH ). It is also supported by the Swiss Data Science Center ( external page SDSC call_made ), which connects academia, industry, and the public sector to address current challenges in data science. The powerful capabilities of Data Science will dramatically shape our future. The programme therefore also offers insights into political, societal, legal, ethical and privacy aspects of Data Science. The target audience for the programme are professionals with work experience and holding a Master's degree in computer science, data science, mathematics, statistics, physics, mechanical engineering, electrical engineering or in a related field. Participants of the programme will learn about state-of-the-art methods and technologies in the field of data science. Graduates of the programme will have a solid basis in the theoretical foundations of data science as well as in the techniques and methodologies required in practical applications. The DAS in Data Science consists in total of 35 to 45 ECTS split over a foundations course, a specialization track, a capstone project, and Download further courses to choose from a list (PDF, 126 KB) vertical_align_bottom . The capstone project gives an opportunity to put the acquired knowledge into practice on real data sets. In order to achieve the basic knowledge the foundation course must be taken in the first semester . At least 12 ECTS have to be acquired in the specialization track before the start of the capstone project.

Foundation course (min. 6 ECTS). One of:

Introduction to Estimation and Machine Learning (6 ECTS - autumn semester)
Introduction to Machine Learning (8 ECTS - spring semester)
Computational Statistics (8 ECTS - spring semester)

Specialization track (mind. 12 ECTS): Courses must be taken from one of the following tracks.

Hardware for Machine Learning
Image Analysis and Computer Vision
Neural Information Processing
Machine Learning and Artificial Intelligence
Big Data Systems

Capstone Project (8 ECTS)

The programme is intended for part-time study programme and starts every semester. The regular study programme duration is 2 semesters, with a leeway of 2 additional semesters. Applicants have to submit a study plan designed over at most 3 semesters as part of their application. Exceptions can be made on request, in which case a motivation must be provided with the application.

Total workload: approx. 1'050 hours, min. 1 year

Course language: English

A Master's degree in computer science, data science, mathematics, statistics, physics, mechanical engineering, electrical engineering or in a related field, as well as existing work experience. Applicants must suggest a Download study plan (DOCX, 50 KB) vertical_align_bottom and in particular indicate their chosen specialization track.

The following documents must be filled in and submitted:

Fully completed Online Application of the School for Continuing Education with an explanation of 150 - 250 words in section "Personal Motivation";
Copy of the Master's degree in German or English (qualifying degrees see above "Admission Requirements");
Copy of the transcript in German or English;
Up-to-date CV or Resume in German or English;
Download Study plan (DOCX, 50 KB) vertical_align_bottom , filled in completely and correctly;
Further documents required by the School for Continuing Education

Optional documents:

English certificate, level C1;
Reference letters

Application period: - Autumn semester: 1 April until 30 April - Spring semester: 1 October until 31 October

Admission Your application will be reviewed by the Admission Committee of the Certificate Programme. Admission requires sufficient scientific fundamental knowledge and pre-knowledge or working experience in the area of data science. The decision is communicated in writing. There is no entitlement to admission.

CHF 12'000.- (incl. CHF 660.- one-time payable semester fee)

Passed courses within the DAS in Data Science programme are accountable for the CAS in Computer Science and the CAS/DAS in Applied Statistics, and vice versa, if applicable. Thus, in compliance with the respective programmes and ETH regulations, it is possible to change from one programme to the other without losing accomplished ECTS points for courses that are accepted as part of the programmes.

Prof. Joachim Buhmann Prof. Helmut Bölcskei Prof. Nicolai Meinshausen Dr. Ghislain Fourny

Is the programme also offered as a distance learning programme? No, the DAS is offered only on-site at ETH. Are there courses offered in the evenings or during the weekends? No, all courses take place on workdays during the day. What is the estimated workload? One credit point equals approximately 30 hours of studying. Where do I find the timetable? There is no predefined timetable. Each student compiles his/her own timetable. The courses of the current and the previous semesters can be found on www.vvz.ethz.ch. Usually, the times of the courses remain the same. Are there additional costs to bare if the regular programme duration of two semester will be exceeded? No, in this case there are no additional costs. It is irrelevant whether a student finishes the programme in two or four semesters

Professors Data Science

Course Programme

Download Course catalogue (PDF, 126 KB) vertical_align_bottom

Download Template Study Plan (DOCX, 50 KB) vertical_align_bottom

Registration Capstone Project

Download Information Registration Capstone Project (PDF, 89 KB) vertical_align_bottom

ETH Zurich Universitätstrasse 6 CAB F 64.1 8092 Zurich Switzerland

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17 Best universities for Data Science in Switzerland

Updated: February 29, 2024

Art & Design
Computer Science
Engineering
Environmental Science
Liberal Arts & Social Sciences
Mathematics

Below is a list of best universities in Switzerland ranked based on their research performance in Data Science. A graph of 506K citations received by 11.2K academic papers made by 17 universities in Switzerland was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Swiss Federal Institute of Technology Zurich

For Data Science

2. Federal Institute of Technology Lausanne

3. University of Zurich

4. University of Geneva

5. University of Bern

6. University of Fribourg

7. University of Lausanne

8. University of St. Gallen

9. University of Basel

10. University of Italian-speaking Switzerland

11. University of Neuchatel

12. University of Applied Sciences Western Switzerland

13. University of Lucerne

14. University of Applied Sciences of Northwestern Switzerland

15. Bern University of Applied Sciences

16. Lucerne University of Applied Sciences

17. University of Applied Sciences and Arts of Italian Switzerland

The best cities to study Data Science in Switzerland based on the number of universities and their ranks are Zurich , Lausanne , Geneva , and Bern .

Computer Science subfields in Switzerland

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Data Science is a modern and interdisciplinary field of research. It is located at the interface between Mathematics and Statistics, Computer Science and Programming.

University of Basel, Data Science, Master

Data Science is extensively applied in various occupational fields ranging from a classical academic career across industry to politics and health care. Nowadays, more and more huge volumes of data sets (“Big Data”) are generated. Therefore, Data Science applies a large variety of different methods like Statistics, Artificial Intelligence, Machine Learning, Data Mining, Deep Learning, algorithms and systems as well as scientific methods to extract knowledge, get insights, analyze and interpret these data. To address these questions, the field encompasses different applications starting with preparing the data sets for analysis, analyzing the data, develop data-driven solutions, and present findings to inform high level decisions in a broad range of application fields. Thus, Data Science is often called to be the fourth pillar in the spectrum of scientific methods, besides theory, research, and computational sciences. All these together makes Data Science a very diversified and fascinating field of research with versatile challenges. Furthermore, you can be creative and place your own ideas to solve problems and generate solutions.

Focal area of teaching and research

The Master’s program Data Science at the University of Basel offers a solid fundamental theoretical knowledge in mathematics and statistics combined with practical applications in managing and processing huge volumes of data sets. Students enrolling to the Master’s program will enjoy a modern curriculum combining the whole data science methods like mathematical foundations, machine learning as well as managing and processing big data with excellent teaching and close links to current research. Due to the synergy with the Center for Data Analytics the students can write their Master’s thesis based on real, cutting-edge data. Graduates of the Master’s degree program are offered a wide range of job opportunities where they can apply their broad-ranging knowledge, e.g. in the IT, pharmaceutical, chemical, and financial industry, banking and insurance companies, museums and lots of other occupational fields. The studies also convey the fundamentals for an entry into research and academia.

Course structure

The Master of Science degree is a postgraduate degree that requires a successfully completed Bachelor’s program. The program awards 120 ECTS credits in total. The Master’s degree program Data Science is a so called mono-course consisting of only one core subject. One ECTS credit point roughly equals 30 hours of studying.

Combination of subjects

The degree programs at the Faculty of Science are generally mono-courses.

Career opportunities

With the qualification of the Master of Science in Data Science, and potentially a subsequent PhD, you acquire an excellent foundation for a very broad range of occupational fields. As the field of Data Science is very interdisciplinary the career opportunities make activities possible in the IT industry as well as in pharmaceutical, chemical and healthcare industry. Research positions in industry and academia, leadership positions in academia and also job opportunities in banking and insurance companies, in the automotive industry, in government and administration as well as in policymaking are feasible.

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Faculty of Business, Economics and Informatics

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Big data and data-based decisions are among the greatest challenges facing us both today and in the future, given the lightning speed at which data is gathered, the vast quantities in which it is stored, and the constant new ways in which it is linked. How are these large and complex stocks of data processed and made usable? And how do you extract knowledge from data?

You have laid a foundation by completing your Bachelor's studies. At Master's level, you will deepen your knowledge through more practical-oriented work. In the Master's study program in Data Science, you will learn how to carry out professional analyses of large data quantities, recognize patterns, demonstrate relationships, and prepare results in appealing, interactive formats.

Drawing on techniques and theories from a range of fields including statistics, informatics, and cognitive sciences, this will give you the foundations for making strategic and operational decisions for individuals, companies, organizations, and society as a whole.

You will also learn how to analyze non-standard problems yourself, how to use independent means of acquiring the knowledge you need to do this, and how to design and use appropriate systems and solutions.

Classes in the Data Science major study program will cover the following topics:

Data Management
Advanced Statistics and Machine Learning
Data Visualization

Career prospects and further degree programs

With a Master's degree in Data Science, you will be one of the few informatics specialists in Switzerland who specialize particularly in analyzing and processing data – a field with a huge amount of potential for the future. The spectrum of potential employers ranges from major companies in the service sector and international IT companies to specialized small firms.

Moreover, graduates with the right aptitude have the opportunity to complete a doctorate, an outstanding foundation for an academic career.

Department of Informatics

Data Science is available as:

Weiterführende informationen, how to proceed.

You plan to pursue a Master's program at our Faculty. Information on the next steps you need to take is available here.

Application and admission: For assuming studies at our Faculty, you first need to apply to the University of Zurich. Application and admission

Admission based on academic background: If you have a Bachelor's degree from another higher education institution, get informed about the categorization process. Admission to the Master's program

Changing program: Being a student at our Faculty, you have come to the conclusion that another study program might fit your interests even better. Changing program

Regulations: You will find everything you need to know about your studies in the regulations. Regulations

Questions on your program of studies: You are pursuing studies at our Faculty and have specific questions? Our academic advisors or the Dean's Office are ready to help you.

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PhD student in Theoretical Chemistry

29 Apr 2024 Job Information Organisation/Company Faculty of Science and Medicine , University of Fribourg Research Field Chemistry » Computational chemistry Researcher Profile First Stage Researcher

Clinical Research Position in Paediatric Oral and Cranio-Maxillofacial Surgery University Hospital Basel

will be part of the Facial and Cranial Anomalies Research Group led by Prof. Dr. Dr. Andreas Müller PhD , MHBA, in affiliation with the Department of Biomedical Engineering and Department of Clinical

PhD Opportunity in Biomedical Ethics and/or Social Psychology

4 May 2024 Job Information Organisation/Company University of Basel Research Field Medical sciences » Other Philosophy » Ethics Psychological sciences » Psychology Researcher Profile First Stage

of an exciting research project on loneliness and social exclusion funded by the Swiss National Science Foundation. The project collects qualitative interview data as well as quantitative panel data

PhD position in computer -aided catalyst design

be carried out in collaboration with groups in theoretical chemistry, computer science and applied mathematics. Job description As a PhD student in our growing team, you will perform quantum-chemical

PhD Position in MRI Physics and Methodology

on the fundamentals of magnetic resonance physics, and on the development of dedicated MR methods and on data post-processing methods for applications in the field of biophysical, biochemical, and clinical sciences

Entry-level position for Technical / Scientific Staff at the Imaging Core Facility (IMCF) of the Biozentrum, University of Basel

17 May 2024 Job Information Organisation/Company University of Basel Research Field Biological sciences » Other Engineering » Other Physics » Biophysics Physics » Optics Researcher Profile First

Postdoctoral position on osteochondral tissue engineering for joint restoration

developing regulatory documentation and manufacturing protocols Organize and conduct animal studies to generate pre-clinical data A PhD in Biomedical Science or a closely related field Strong motivation and

environment The PhD position is fully funded for a maximum of 4 years in line with employment conditions established by the Swiss National Science Foundation. Application / Contact For further information about

Biozentrum PhD Fellowships – Summer Call 2024

19 Mar 2024 Job Information Organisation/Company University of Basel Research Field Biological sciences » Biology Biological sciences » Other Chemistry » Biochemistry Medical sciences » Other

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37 PhD jobs in Switzerland

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PhD positions in Zurich (16)
PhD positions in Basel (8)
PhD positions in Dübendorf (6)
PhD positions in Geneva (2)
PhD positions in Neuchâtel (2)
PhD positions in Lugano (1)
PhD positions in Birmensdorf (1)
PhD positions in St. Gallen (1)

Other countries

PhD positions in Belgium (153)
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PhD positions in Luxembourg (27)
PhD positions in Sweden (23)
PhD positions in Austria (19)
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PhD positions in Finland (13)
PhD positions in Norway (9)
PhD positions in Israel (4)

Search results (37)

Assistenzstelle (Doktorandenstelle) Geschlechterforschung 60 %

Das Zentrum Gender Studies der Universität Basel sucht per 1. August 2024 oder nach Vereinbarung eine wissenschaftliche Assistenz (Doktorandenstelle, 60%) in der Geschlechterforschung. Realisation ...

Doctoral (PhD) Student Positions in Control for Advanced Manufacturing

Doctoral (PhD) Student Positions in Control for Advanced ManufacturingWith our cutting-edge research, ETH Zürich's around 12’000 employees make essential contributions to the well-being of society ...

2 PhD positions in immersive architectural design and spatialized sound

2 PhD positions in immersive architectural design and spatialized soundThe Immersive Models research project is an interdisciplinary collaboration between Gramazio Kohler Research at the Federal In...

PhD student for the further development and validation of a novel sensor selection methodology for autonomous driving applications

Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living. ...

Doctoral Position in European Global Studies

The University of Basel has an international reputation of outstanding achievements in research and teaching. Founded in 1460, the University of Basel is the oldest university in Switzerland and ha...

International Fully Funded PhD Positions in the Life Sciences in Switzerland

*New application deadlines for two open calls per year: November 1 and May 1!Life Science Zurich Graduate School offers more than 100 funded PhD positions. With around 500 research groups and more than 1600 Ph.D. students, the Life Science Zurich ...

PhD Position in Digital Identities for Regenerative Materials

PhD Position in Digital Identities for Regenerative MaterialsThe Chair of Circular Engineering for Architecture (CEA) at ETH Zurich is seeking a highly motivated and qualified individual to fill a ...

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

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Doktorand*in 100%

Die Forschungsgruppe für Translationale Medizin und Atemanalyse am Department of Biomedical Engineering (DBE) der Universität Basel sucht eine*n motivierte*n Doktoranden*in für ein spannendes Proje...

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PhD Percentage by Country / Number of Doctorate Degrees per Country 2024

1. slovenia.

Slovenia is the highest-ranking country in the number of Ph.D. degrees earned by adults between the ages of 25 and 64. 5% of the entire population in Slovenia has a Ph.D. The distribution between Ph.D. adults in Slovenia finds more female doctorates than males. About 4.5% of adult females have a Ph.D., compared to about 3% of adult males in Slovenia.

2. Switzerland

Switzerland has the second-highest rate of Ph.Ds. globally, with nearly 3% of the adult population holding this advanced degree. In Switzerland, men are more likely to hold a Ph.D., representing 4% of the total doctorate population, compared to about 2.5% for female doctorates.

3. Luxembourg

In Luxembourg, 2% of the total population holds a Ph.D. There are several countries tied for 2% of the population with a Ph.D., and almost all of the highly educated countries are in Europe . In Luxembourg, 2.5% of Ph.D. holders are male, compared to 2% female.

4. United States

The United States is tied with European countries when it comes to being a highly educated population. About 2% of the U.S. population holds a Ph.D. Men are more likely than women to have a Ph.D. in the United States, with 2.5% of the Ph.D. candidates male and about 2% female.

Like other countries on our list, Sweden is tied with 2% of the total population holding a doctorate degree. In this country, males and females are just about equal when it comes to this advanced degree. Around 2.5% of males have a Ph.D., while 2.2% of females hold a Ph.D. in Sweden.

6. The United Kingdom

Rounding out our list of highly educated countries with the most Ph.Ds., the United Kingdom has about 2% of the population holding a Ph.D. In the United Kingdom, males and females are pretty close to equal in higher education. Males account for more Ph.D. degrees, with about 2.5%, while females are close behind with nearly 2.3% Ph.Ds.

Another European country on our list of the most Ph.Ds., Germany, has about 2% of its population with a Ph.D. This country sees a more significant discrepancy between males and females when it comes to higher education. About 1.75% of males account for Ph.Ds. awarded, while only about 1% of females have their Ph.D. in Germany.

8. Australia

Breaking up the stranglehold at the top of our list between Europe and the United States, Australia makes the list with the 8th most Ph.D. citizens per capita. In Australia, 2% of the population holds a doctorate degree. Males and females are almost identical regarding Ph.Ds., with males just slightly earning a Ph.D. more frequently. Males account for 1.1% of Ph.Ds. in the country, while females account for 1.0% of the Ph.Ds. awarded in Australia.

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What country has the highest percentage of PhDs?

Frequently asked questions.

What are the characteristics and outcomes of doctoral graduates? - OECD
These countries have the most doctoral graduates - World Economic Forum
List of countries by number of doctorates awarded - Wiki

Department of Mathematical Modeling and Machine Learning

Phd data science, phd programme in data science, graduate school for data science.

Requirements

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

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 ICS (PDF, 238 KB)

Checklist for PhD students

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

Institute for Computational Science "Acceptance Confirmation Structured Doctoral Program" (PDF, 136 KB)

Annual committee meeting minutes (PDF, 927 KB)

Confirmation Sheet - TA Hours (PDF, 107 KB)

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

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

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College of Liberal Arts and Sciences

Department of Psychological Sciences

Post-doctoral senior expert in data analysis.

Application Deadline: September 1, 2024

The Autism, Bilingualism, Cognitive and Communicative Development (ABCCD) group in Fribourg, Switzerland, is looking for a post-doctoral senior expert in data analysis to join their team for 2 years (starting in September 2024), preferably at 50%, although this is open to discussion (42,260 chf per year salary for 50%). This member of our team will bring expertise in statistics and will be instrumental in the discussion and support of ongoing data analyses, training/ advice in the use of specific analysis techniques and interpretation of the results along with the team members. The candidate will also be comfortable in academic writing in English as s/he will play an integral role in preparing/co-authoring manuscripts for publication, thus a solid track-record attesting to academic writing skills is required.

Our collaborator will have the necessary background to fully grasp our research questions on bilingualism and the methods applied to address them. Most studies underway in our group are concerned with elucidating the impact of bilingualism on various aspects of development (cognition, communication) in both typically developing children as well as those with autism. We are seeking someone to help us decide on the most suitable statistical techniques to analyze these very complex relations, capable of incorporating both cross-sectional and longitudinal data to evaluate the relevance of bilingualism as a predictor of our dependent variables, integrating both continuous and categorical predictors at all levels of measurement (i.e., subject- and time-varying), and handling missing data (e.g., due to some children not completing all tasks in the protocol, dropout, etc). Ideally, s/he would be comfortable with an array of techniques (linear mixed effect regression, Bayesian Analysis, Structural Equation Modeling, cluster analyses, Latent Class Analysis, Deviation Sum Coding…).

Our post will thus bring expertise in statistics and be instrumental in the discussion and support of ongoing data analyses, training in the use of specific analysis techniques and interpretation of the results along with the team members. S/he will play an integral role in preparing/co-authoring manuscripts for publication, and conducting analyses of both cross-sectional and longitudinal data, integrating both continuous and categorical predictors at different levels of measurement (i.e., subject- and time-varying), and handling missing data (e.g., due to some children not completing all tasks in the protocol, dropout, etc). This member of our team will also help to conceptualize future projects with the PI that can be developments of the current work, and potentially assist with grant writing related to this.

Interested candidates should send a CV (including 2 potential references) and letter of motivation to [email protected]

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Department of Astrophysics old

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

Requirements

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

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 ICS (PDF, 238 KB)

Checklist for PhD students

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

Institute for Computational Science "Acceptance Confirmation Structured Doctoral Program" (PDF, 136 KB)

Institute for Computational Science "Doctoral Agreement" (PDF, 908 KB)

Annual committee meeting minutes (PDF, 927 KB)

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

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

Composite image of 2024 Waterloo.AI Scholarship Winners

Meet the 2024 Waterloo.AI Graduate Scholarship Winners

Learn more about the four winners of the $5,000 Waterloo.AI 2024 Graduate Scholarship

Each year, Waterloo.AI awards $5,000 scholarships to graduate students conducting exceptional research in AI. Here are the 2024 winners and their fields of research.

Masha Golchoubian - PhD Candidate (ENG - Systems Design)

Mahsa's PhD research focuses on developing a human-aware navigation algorithm for autonomous vehicles operating in pedestrian-rich environments. She uses AI techniques to predict pedestrian trajectories in interactive settings and employs these predictions to create an interaction-aware deep reinforcement learning motion planner that accounts for the uncertainty in pedestrians' future behaviors. The designed decision-making algorithm promotes safe, farsighted, and socially compliant vehicle navigation among pedestrians, aiming for a future where autonomous agents can coexist safely with humans.

Olha Anastasia Wloch - Master's Student (MATH - Computer Science)

Olha is currently pursuing her master’s degree under the supervision of Dr. Lukasz Golab and Dr. Robin Cohen. Her research focus is on building Explainable AI (XAI) frameworks and applications which directly help healthcare practitioners. Currently, one of the biggest bottlenecks of AI integration within fields such as healthcare is trust and reliability in these models. As the potential of AI models increases, our understanding of how these models make decisions decreases. Olha’s research is working towards developing XAI tools that help with the clinical decision-making process. These tools can decrease hospital length of stay and mitigate physician burnout and intra-physician variability in decision making.

Yudong Luo - PhD Candidate (MATH - Computer Science)

Yudong’s Ph.D. research focuses on Reinforcement Learning, an emerging field within AI, under the guidance of Professor Pascal Poupart. His work delves into the problem of time-sequential decision-making under uncertainty and risk. His research applies to various real-world domains necessitating risk-aversion and safety considerations, such as minimizing substantial financial losses in portfolio management and ensuring safe behaviour in autonomous driving scenarios.

Shawn Reeves - PhD Candidate (SCI - Chemistry)

Shawn's Ph.D. research is in biochemical engineering under the supervision of Professor Subha Kalyaanamoorthy. His research seeks to leverage deep learning to improve the properties of proteins, which are renewable, functional biomaterials. He is specifically focused on improving the activity, solubility, and stability of catalytic proteins (enzymes) used in industrial processes, including carbon dioxide sequestration and plastic recycling.

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Professional Science Masters (PSM)

Graduate school, main navigation.

Professional Masters in

Computational & Data Science

Are you looking to grow as a professional? Do you want to improve your communication, leadership, and collaboration skills? Explore mathematical models, numerical methods, and data visualization, which are in high demand. You will learn to tackle real-world problems with the full range of skills from these fields.

PSM Admissions

Transferrable Business Skills

(12 credits)

Advanced Quantitative Skills

(6 Credits)

Computational & Data Science Courses

(15 Credits)

Professional Experience Project

(3 Credits)

36 credits & a GPA Higher than 3.0

In graduate school, 9 credits and up is full-time. Full-time students can complete the program in 2 years. Part-time students take 2.5 to 4 years to complete the program.

Transferrable Skills Courses

Data science, computational science.

Expect to take these courses with a cohort of peers from across the different PSM degree tracks.

You may need permission codes to register for classes taught by other departments. Contact advisors in those departments to get those codes. You can find a list of these courses on the PSM canvas course under Module 3: Program of Study.

Professional Development for Scientists & Engineers

MST 6200 - 3 Credits - Fall

Operations & Project Management for Scientists & Engineers

MST 6210 - 3 Credits - Spring

Scientific Reasoning & Inquiry

MST 6500 - 3 Credits - Spring

Business Development for Scientist & Engineers

MST 6110 - 3 Credits - Summer

Applied Statistical Techniques

MST 6600 - 3 Credits - Fall

Core Requirements

Introduction to probability.

MATH 5010 - 3 Credits - Fall

Statistical Inference I

MATH 5080 - 3 Credits - Fall

Graduate Algorithms

CS 6150 - 3 Credits

Advanced Database Systems

CS 6530 - 3 Credits - Fall

Data Mining

CS 6140 - 3 Credits - Fall

Machine Learning

CS 6350 - 3 Credits - Fall

Visualization for Data Science

CS 6630 - Credit 3 - Fall

Focus Area Electives

Students can choose courses from the COMP and CS MATH areas based on their professional goals.

Course availability is subject to change. Substitute classes are available, upon approval. Courses may have prerequisites. Students are responsible for confirming they meet course requirements and prerequisites. These courses are in the General Catalog .

MATH 5080 - 3 Credits - Fall

Statistical Inference II

MATH 5090 - 3 Credits - Fall

Introduction to Numerical Analysis I

MATH 5610 - 4 Credits - Fall

Introduction to Numerical Analysis II

MATH 5620 - 4 Credits - Fall

Analysis of Numerical Methods I

MATH 6610 - 3 Credits - Fall

Analysis of Numerical Methods II

MATH 6620 - 3 Credits - Fall

Introduction to Applied Mathematics I

This project is a critical component of any PSM program & required for graduation.

It is a hands-on project in a real work environment of business & science.

Professional Experience Project Planning

MST 6974 - 1 Credits - Fall/Spring

MST 6975 - 3 Credits - Fall/Spring

This course teaches business management & development skills.

Students will learn:

Modern business practices
How to create and use effective business plans
To make business forecasts and scenarios
To be effective managers
Marketing and sales strategies
Financial planning and analysis

This course teaches scientists & engineers how to excel in their careers.

This course includes:

Interactive lectures
Practical exercises
Real-world case studies
Self-assessments
To improve their professional abilities
To be better communicators
How to advance their careers

This is a cohort class for first-year MST students.

This course offers functional skills for & the improvement of organizational processes.

To manage value creation
Effective and efficient process design
Operational design and theory
To plan and organize functions of management
To carry out and oversee functions of management

This course teaches scientific reasoning, inquiry, & problem-solving skills.

Topics covered include:

Simple and theoretical induction
Bayesianism
Statistical and causal hypotheses
Using scientific information in decision making
Inference to the best explanation
Science and the individual
Science and society

Required of all first-year MST students.

This course teaches exploratory data analysis (EDA) & the R coding language.

Real-world examples
Scatter plots
Probability plots
Residual plots
The RStudio platform
Standard and quantitative data evaluation techniques

No prior knowledge of the R coding language needed and you should bring your laptop.

This course assists with methods and skills needed to complete a professional project.

Realistic goal setting
Project initiation
To define objectives and scope
To identify and manage risk
Resource allocation
To keep track of progress

Professional Project

This course bridges the gap between theoretical knowledge and practical skills..

Students will be able to apply diverse skills gained from their studies such as:

Problem-solving
Critical thinking
Communication
Project management
Collaboration

This course provides an introduction to the fundamental concepts of probability.

Key topics include:

Combinatorial problems
Random variables
Distributions
Independence and dependence
Conditional probability
Expected value and moments
Law of large numbers
Central limit theorems

CS 6150 - 3 Credits - Fall

This course provides an in-depth study of algorithms, focusing on design and analysis.

Greedy algorithms
Dynamic programming
Divide and conquer strategies
Asymptomatic analysis and recurrence relations
Graph algorithms and network flows
Computational complexity and intractability
NP-hardness and beyond
Approximation algorithms

3 Credits - Fall

This graduate-level course focuses on the design and implementation of relational database system kernels and other large-scale data management techniques.

Relational data model and SQL
File organization, database storage, indexing and hashing
Query evaluation and optimization
Transaction processing, concurrency control and recovery
Database integrity and security
Latest developments in large-scale data management techniques

Students will participate in a semester-long project to build a mini-database system. Note: This is not a course on building database applications.

Data Mining is about discovering patterns and structures in large data sets. This course will guide you on how to model these problems and find solutions using efficient algorithms. Some of these methods involve the use of randomized algorithms, which are simple to use but can be tricky to analyze. We'll focus on how to use them effectively and provide clear explanations.

This course covers techniques for developing computer programs that can acquire new knowledge automatically or adapt their behavior over time.

The curriculum includes a variety of algorithms for both supervised and unsupervised learning. You'll learn about decision trees, online learning, and linear classifiers.

The course also explores:

Methods to minimize empirical risk
Computational learning theory
Ensemble methods
Bayesian methods
Techniques for clustering and dimensionality reduction

CS 6630 - 2 Credits - Fall

This course introduces the principles, methods, and techniques for effective visual analysis of data as applied to data science.

We will explore aspects of visualization related to tabular (high-dimensional) data, graphs, text, and maps.

This course also explores:

Bootstrapping the necessary technical skills (web development with HTML5 and JavaScript)
Overview of principles from perception and design
Visualization fundamentals such as interactions and views
Visualization techniques and methods for non-spatial data types and maps
Continual analysis, critique, and redesign of visualizations
Hands-on experience designing and implementing interactive, web-based visualizations using cutting-edge visualization libraries

A complementary course - Visualization for Scientific Data - that focuses on the visualization of spatial data (e.g., grid-based data from simulations and scanning devices) is offered in the spring.

This course is designed to equip students with the mathematical tools necessary for understanding randomness and uncertainty.

The course covers:

Problem-solving techniques in combinatorics
Understanding and working with random variables and their distributions
Concepts of independence and dependence
Conditional probability and its applications
Calculation and interpretation of expected values and moments
Insights into the law of large numbers
Introduction to central limit theorems

This course is the first part of a comprehensive introduction to statistical inference, focusing on the theoretical foundations of statistics.

Principles and techniques of sampling
Understanding sampling distributions
Application and interpretation of the Central Limit Theorem
Methods for data transformation
Concepts of complete and sufficient statistics
Techniques for point estimation
Principles of optimality in statistical inference

This course provides an introduction to the fundamental concepts of numerical analysis. It is designed for students with prior programming experience.

Numerical methods in linear algebra
Techniques for numerical interpolation
Numerical integration and differentiation
Approaches to approximation, including discrete and continuous least squares, Fourier analysis, and wavelets

This course is a continuation of Introduction to Numerical Analysis I. It delves into the numerical solution of initial value problems in ordinary differential equations and introduces students to numerical approaches for solving partial differential equations.

Analysis of Numerical Methods I

This course explores initial- and boundary-value problems of ordinary and partial differential equations.

The course also covers:

Linear algebra
Interpolation
Integration
Differentiation
Approximation techniques including least squares, Fourier analysis, and wavelets

This course is a continuation of Analysis of Numerical Methods I.

MATH 5710 - 3 Credits - Fall

This course covers key mathematical concepts and techniques used in various fields, including computer science and data analysis.

The course focuses on:

Understanding and solving symmetric linear systems
Working with positive definite matrices
Solving eigenvalue problems
Understanding equilibrium equations for both discrete and continuous systems
Solving boundary value problems in ordinary and partial differential equations
Applying boundary integrals

This advanced course is designed for students who seek a deeper understanding of statistical theory and its applications.

The course covers a range of topics including:

Estimation of intervals
Testing of hypotheses
Methods based on likelihood
Understanding of errors and optimality
Study of order statistics
Introduction to nonparametric methods
Exploration of rank statistics

Scientists Connect 16 Mini Brains Made of Human Tissue to Create a "Living Computer"

It's like a 21st century version of frankenstein's monster..

Switzerland-based startup FinalSpark claims to have built a unique computer processor made from 16 mini brains made from human brain tissue, Tom's Hardware reports — and they are positioning this "living computer" as an alternative to silicon-based computing.

And now, other researchers can remotely access the startup's biocomputer, the Neuroplatform, to conduct studies on, say, artificial intelligence, which typically requires enormous resources .

"One of the biggest advantages of biological computing is that neurons compute information with much less energy than digital computers," FinalSpark scientist and strategic advisor Ewelina Kurtys wrote in a company blog post earlier this month. "It is estimated that living neurons can use over 1 million times less energy than the current digital processors we use."

The startup takes brain organoids , small samples of human brain tissue derived from neural stem cells, and places them in a special environment that keeps these organoids alive. They then hook up these mini brains to specialized electrodes to perform computer processing and digital analog conversions to transform neural activity into digital information.

The concept of living computers has been around for quite some time now. Last year, for instance, scientists hooked up neurons to electrical circuits, resulting in a device that could perform voice recognition .

These unusual machines have some noteworthy advantages over their silicon-based counterparts, including a significantly smaller carbon footprint.

"This is one of the reasons why using living neurons for computations is such a compelling opportunity," Kurtys said. "Apart from possible improvements in AI model generalization, we could also reduce greenhouse emissions without sacrificing technological progress."

FinalSpark hopes other institutions will tap its Neuroplatform in order to advance biocomputer research, while positioning this tool as the next step in AI computing.

As AI companies clamor for resources for data centers, with concerns growing over carbon emissions and water , it's a novel approach that may just pay off in the long run.

More on brains: Computer Made From Human Brain Cells Can Perform Voice Recognition

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How Science, Math, and Tech Can Propel Swimmers to New Heights

One hundred years ago, in the 1924 Paris Olympics, American Johnny Weissmuller won the men’s 100m freestyle with a time of 59 seconds. Nearly 100 years later in the most recent Olympics, the delayed 2020 Games in Tokyo, Caeleb Dressel took home the same event with a time that was 12 seconds faster than Weissmuller’s.

Swimming times across the board have become much faster over the past century, a result of several factors, including innovations in training, recovery strategy, nutrition, and some equipment advances.

One component in the improvement in swimming performances over the years is the role of biomechanics — that is, how swimmers optimize their stroke, whether it's the backstroke, breaststroke, butterfly, or freestyle.

Swimmers for decades have experimented with different techniques to gain an edge over their competitors. But in more recent years, the application of mathematics and science principles as well as the use of wearable sensor technology in training regimens has allowed some athletes to elevate their performances to new heights, including members of the University of Virginia’s swim team.

In a new research paper , a UVA professor who introduced these concepts and methods to the team and some of the swimmers who have embraced this novel approach to training lay out how the use of data is helping to transform how competitive swimmers become elite.

‘Swimming in Data’

Ken Ono thought his time working with swim teams was over. Ono — a UVA mathematics professor, professor of data science by courtesy, and STEM advisor to the University provost — had spent years working with competitive swimmers, first during his time at Emory University in Atlanta and then with other college teams, including Olympians, over the years.

However, he didn’t plan to continue that aspect of his work when he arrived at UVA in 2019. But after a meeting with Todd DeSorbo, who took over the UVA swim program in 2017, Ono soon found himself once again working closely with athletes, beginning his work as a consultant for the team during the 2020-21 season . The UVA women’s swim team would win their first of four consecutive national championships that year.

“One of the things that I like quite a bit about this work is that swimming is crazy hard,” Ono said. “We were never meant to be swimmers, and it is both an athletic challenge as well as a scientific challenge — it has it all.”

Last fall, following a suggestion from DeSorbo, Ono offered a class that outlined the science-focused approach to improving swimming performances that had proven so successful at UVA, but he wanted to make sure there were no misconceptions about the seriousness of the material.

“We don’t want people thinking that it’s a cupcake course that’s offered for the swimmers,” Ono said.

So, Ono teamed up with UVA students Kate Douglass, August Lamb, and Will Tenpas, as well as MIT graduate student Jerry Lu who had worked with Ono and the UVA swim team while an undergraduate at the University, to produce a paper that covered the key elements of the class and Ono’s work with swimmers.

Tenpas and Lamb both recently completed the residential master’s program at the School of Data Science as well as their careers as competitive collegiate swimmers. Douglass, who finished her UVA swim career in 2023 as one of the most decorated swimmers in NCAA history, is a graduate student in statistics at the University and is set to compete in the Paris Olympics after winning a bronze medal in the 2020 games.

The group drafted the paper, which they titled “Swimming in Data,” over the course of two months, and it was quickly accepted by The Mathematical Intelligencer. There, Ono said, it has become one of the most-read papers on a STEM subject since tracking began. In July, a version of the paper will also be published in Scientific American.

“It seems to have taken off,” Ono said.

The impact of digital twins

After outlining the evolution of swimming over the past 100 years, the paper explains how an understanding of math and physics, combined with the use of technology to acquire individual-level data, can help maximize performances.

Essential to understanding the scientific principles involved with the swimming stroke, the paper says, are Newton’s laws of motion. The laws — which cover inertia, the idea that acceleration depends on an object’s mass and the amount of force applied, and the principle that an action exerted by an object on another elicits an equal and opposite reaction — help simplify how one should think about the many biomechanical factors involved with swimming, according to Tenpas.

“There are all sorts of flexibility limitations. You have water moving at you, you have wakes, you have currents — it’s easy to kind of get paralyzed by the number of factors,” said Tenpas, who after four years at Duke, where he studied mechanical engineering, enrolled in UVA’s data science program and joined the swim team with a fifth year of eligibility.

“I think having Newton’s laws is nice as it gives you this baseline we can all agree on,” he added.

It’s a way to understand pool mechanics given the counterintuitive motion swimmers must use to propel themselves forward, according to Ono.

“The reason that we go to great extent to recall Newton’s laws of motion is so that we can break down the factors that matter when you test a swimmer,” he said.

To conduct these tests, Ono and his team use sensors that can be placed on swimmers’ wrists, ankles, or backs to gather acceleration data, measured as inertial measurement units. That information is then used to generate what are called digital twins, which precisely replicate a swimmer’s movements.

These twins reveal strengths and weaknesses, allowing Ono and the coaching staff to make recommendations on technique and strategy — such as how to reduce drag force, a swimmer’s true opponent — that will result in immediate improvement. In fact, through the analysis of data and the use of Newton’s laws, it is possible to make an accurate prediction about how much time a swimmer can save by making a given adjustment.

Lamb, who swam for UVA for five years while a computer science undergrad then as a data science master’s student, likened digital twins to a feature in the popular Nintendo game Mario Kart where you can race against a ghost version of yourself.

“Being able to have this resource where you can test at one month and then spend a month or two making that adjustment and then test again and see what the difference is — it’s an incredibly valuable resource,” he said.

To understand the potential of digital twins, one need only look at the example of Douglass, one of the co-authors, which is cited in the paper.

A flaw was identified in her head position in the 200m breaststroke. Using her digital twin, Ono and the coaching staff were able to quantify how much time she could save per streamline glide by making a modification, given her obvious talent and aerobic capacity. She did, and the results were remarkable. In November 2020, when her technique was tested, the 200m breaststroke wasn’t even on her event list. Three years later, she held the American record.

‘Everyone’s doing it now’

Swimming will be front and center in the national consciousness this summer. First, the U.S. Olympic Team Trials will be held in Indianapolis in June, leading up to the Paris Olympics in July and August, where DeSorbo, UVA’s coach who embraced Ono’s data-driven strategic advice, will lead the women’s team.

Many aspiring swimmers will undoubtedly be watching over the coming weeks, wondering how they might realize their full athletic potential at whatever level that might be.

For those who have access to technology and data about their technique, Tenpas encouraged young swimmers to take advantage.

He noted the significant amount of time a swimmer must put in to reach the highest levels of the sport, estimating that he had been swimming six times per week since he was 12 years old.

“If you’re going to put all of this work in, at least do it smart,” Tenpas said.

At the same time, Lamb urged young swimmers who may not yet have access to this technology to not lose faith in their potential to improve.

“While this is an incredibly useful tool to make improvements to your technique and to your stroke, it’s not the end all, be all,” he said.

“There are so many different ways to make improvements, and we’re hopeful that this will become more accessible as time goes on,” Lamb said of the data methods used at UVA.

As for where this is all going, with the rapidly expanding use and availability of data and wearable technology, Ono thinks his scientific approach to crafting swimming strategies will soon be the norm.

“I think five years from now, our story won’t be a story. It’ll be, ‘Oh, everyone’s doing it now,’” he said.

MSDS Student Profiles: August Lamb and Will Tenpas on Balancing Swimming and Graduate School

Ken Ono Talks About Using Data to Improve Swimmer Performance on CBS19 for “Inside The Numbers”

Data Science Master’s Students Tackle Diverse, Real-World Challenges in Capstone Projects

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Statistics and Actuarial Science

Information for new graduate students in actuarial science, data science and statistics at the university of iowa..

Welcome New Graduate Students!

Information for NEW graduate students in Actuarial Science, Data Science and Statistics at the University of Iowa.

Last Updated, May 31, 2024. Additional updates will be sent this summer!

Important Information for International Students

The Office of International Students and Scholars does an incredible job helping you settle into Iowa City and the University of Iowa. They have webinars to help with:

1. Getting Started and Making Travel Arrangements

2. Achieving Success: On-campus Involvement and Cultural Adjustment (undergraduate students)

3. Graduate Student Professionalization and Support

4. Understanding Orientation Expectations, Responsibilities, and Placement Tests (graduate students)

5. On-campus Housing Assignments and Move-in Tips (undergraduate students)

6. Student Employment

7. Money Matters - University Billing

Do you need to take the SPEC (Spoken Proficiency of English for the Classroom)?

All students for whom English is not a first language (as self-reported on their admissions application) and who have first-time appointments as graduate teaching assistants (TAs) are required to go through a testing process to assess their effectiveness in speaking English before they are assigned assistantship responsibilities. Beginning in Fall 2024, there will be a new test to assess communication in English in a classroom context called SPEC (Spoken Proficiency of English in the Classroom). This is replacing ESPA and ELPT. Details will be coming soon.

Any graduate student who is included in the following categories needs to have their oral English proficiency tested by the TAPE Program:

Students whose first language is not English (i.e., learned another language first) as self-reported on their admissions application, and
Have been appointed as a Teaching Assistant

Exemptions (may change):

Students with an official valid (within the last two years) iBT Listening score of 25 and an iBT Speaking score of 26.
Undergraduate degrees and/or
Continuous attendance of English-language schools since the age of 12 (or younger)
Students who served as teaching assistants at other institutions of higher learning in which the language of instruction is English, if they were listed as the instructor of record for a course or led a discussion section in English for at least one year, with a year defined as either two academic semesters or three academic quarters.
Requests for exceptions regarding the SPEC can be submitted for evaluation to a committee consisting of the Director of ESL Programs, the Associate Dean for Administrative Affairs in the Graduate College, and a representative from University Human Resources.

Requests for exemption and exceptions must come from the department by the deadline, not the student. Deadlines to register students for the SPEC are:

March 1

NOT Exemptions:

Students who come from a country where English is one of the official languages.
Students who are U.S. permanent residents or U.S. citizens whose first language is not English.

Testing Procedures & Results

To be announced soon!

Graduate/Professional International Students Important Dates

July 12, 2024: Earliest date you may enter the U.S. in F-1 or J-1 status. August 11, 2024: Latest date by which you should arrive in Iowa City August 12 - 16, 2024: International Student Orientation August 26, 2024: Classes begin.

Housing Information for All Students

The department has a housing webpage, please let us know if you have any questions or concerns. If you are looking for a roommate, please let us know and we can update this web page!

Looking for housing options ?

All US citizens that are financially supported (TA, RA) need to be here on August 21.

All students will register for classes the week before classes start. International students must complete the required Orientation Program before they can register for classes.

____________________

Fall Classes Advising will be August 19-23

All NEW UI students must meet with their advisor prior to registration. There is no worry about getting into any of the classes we teach.

IF you are an Actuarial Science MS or PhD student you will need to meet with Professor Shyamalkumar. Email him after August 12 at [email protected] to set a time to meet to discuss what classes to take, it may be on Zoom or in his office (233 Schaeffer Hall).
IF you are a Data Science MS, Statistics MS, or PhD student you will need to meet with Professor Boxiang Wang. Email him after August 12 at [email protected] to set a time to meet to discuss what classes to take, it may be on Zoom or in his office (261 Schaeffer Hall).

New Graduate College Welcome and Orientation, August 21

The Graduate College Fall 2024 Graduate Student Orientation event will take place on Wednesday, August 21, 2024. A registration form will be sent to your UI email sometime this early summer from the Graduate College. All new doctoral and master’s students are invited to attend.

New Teaching Assistant Orientation, August 22- required for all new supported students

New Student Department Orientation, August 23 at 9 a.m., Room to be determined.

All New Student Orientation —Group Introductions and General Policy Procedures.

New Supported Graduate Assistants Orientation, August 23 at 1 p.m., Room to be determined.

Our Director of Graduate Studies will have a department review of expectations and your specific roles in our department. Teaching and grading assignments will be explained, as well as preparation, teaching tips, problems and questions, quizzes and exams, weekly meetings, grading, appropriate office use and the Sexual Harassment Prevention Education

Mailbox in 241 Schaeffer Hall

All graduate students will have a mailbox in our main office. The faculty do as well. Please check your mailbox at least once a week!

Office Desk Assignment

Nearly all supported students will have a desk in one of our offices. The assignment priority (in this order) includes Ph.D. and Fellowship candidates, research assistants, half-time teaching assistants, quarter-time teaching assistants and lastly graders. Having a desk is a privilege and should be used only for university business. Office assignments will be given to students on, August 23. Keys are checked out ONLY after that time. Please remember to keep the rooms clean and take out all trash to the large bins in the main hallways.

Set-up your University of Iowa Email

All University of Iowa students are required to activate their assigned uiowa.edu email address, as all official communication from university offices are now sent via email, rather than hard copy. This address usually follows the pattern [email protected] (However, often a number is also attached.)

To activate the account:

Log on to MyUI
Click on My UIowa / My Email / Request Email Account
Complete the specified steps.

Students who prefer to maintain only their work or home email addresses can do so by routing the uiowa.edu email to a work or home account. To do so, follow these steps:

Click on My UIowa / My Email / Update Email Routing Address

Important Notes:

If your uiowa.edu email address is routed to a different account, you will not need to change your address in ICON, as your messages will already forward to your routed address.
Log on to MYUI.
Click on My UIowa / My Email / Email Account Filter bulk mail.
Make sure that none of the categories are checked.

Required Graduate Assistants Teaching Courses:

ONLINE CLASS Requirement: Sexual Harassment Prevention Edu. Use your HawkID and password to log into Employee Self Service. Click the Personal tab, next (under Learning and Development) click on Sexual Harassment Prevention Edu., follow instructions.
ONLINE CLASS Requirement: Federal Educational Rights and Privacy Act (FERPA), Use your HawkID and password to log into Employee Self Service. Click the Personal tab, next (under Learning and Development) next click on Available Online Icon Courses, next FERPA Training, then click on View Details twice and the last click will be to Enroll in this ICON Course Session.
A six-hour orientation program will be required of all students who are certified at level A or B and are teaching for the first time. This orientation helps new teaching assistants understand the culture of the U.S. classroom and treats topics such as student expectations, teacher-student relationships, and understanding and answering student questions. Discussion focuses on suggestions for maximizing comprehensibility in spoken English. This course meets twice for 3 hours early in the semester. Both meetings are held in the evening.

Administrative Department Staff:

Professor aixin tan (until july 1, 2024).

Director of Graduate Studies, Statistics and Data Science Graduate Advisor: [email protected] (319) 335-0821.

Professor Boxiang Wang (beginning July 1, 2024)

Director of Graduate Studies, Statistics and Data Science Graduate Advisor: [email protected] (319) 335-2294.

Professor N.D. Shyamalkumar

Actuarial Science Graduate Advisor: [email protected] (319) 335-1980

Margie Ebert

Academic Services Coordinator , [email protected] (319) 335-2082

Heather Roth

Administrative Services Coordinator [email protected] (319) 335-0712

Tammy Siegel

Department Administrator , [email protected] , (319) 335-0706

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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 ...
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The aim of the PhD Programme in Data Science is to offer students with a master degree (including degrees from an university of applied sciences) the opportunity to obtain a PhD in cooperation between a university of applied sciences and a university. The PhD Programme in Data Science is a collaboration between ZHAW Zurich University…
UZH
The main requirement for admission to doctoral study is a Master's degree from a university or an equivalent degree. Each PhD project must receive approval from a professor at the Faculty of Science or from a person who has the right to confer a PhD at the Faculty of Science. There is no general right to be granted admission to a PhD program.
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The ZHAW School of Engineering is the leading house of the Swissuniversities-funded PhD Network in Data Science. Three of the departments of ZHAW (the Schools of Management and Law, Life Science and Facility Management, and Engineering), three faculties of the University of Zurich (the Faculties of Mathematics and Natural Sciences, Economics, and Philosophy), the Faculty of Science of the ...
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Application
Modus Operandi. The student will carry out the PhD research and has a co-supervisor at ZHAW.In addition, the student will be supervised by a Professor at UZH.The UZH supervisor must be associated with the Faculty of Science (MNF). The student will be enrolled as PhD student in the PhD programme in data science at UZH. The duration of the PhD is typically 4 years.
PhD programme
ZHAW is part of the PhD Network Data Science.We thus participate in the University of Zurich's PhD Programme in Data Science. A broad selection of topics is available for doctoral studies in data science and these explicitly include data-intensive areas of Industry 4.0, such as predictive maintenance, data analytics and risk management, and cloud computing.
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The Master of Science degree is a postgraduate degree that requires a successfully completed Bachelor's program. The program awards 120 ECTS credits in total. The Master's degree program Data Science is a so called mono-course consisting of only one core subject. One ECTS credit point roughly equals 30 hours of studying.
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The Life Science Zurich Graduate School, for example, offers this kind of programme. There are 16 highly competitive doctoral programmes, run jointly by ETH Zurich and the University of Zurich. The aim is to attract the most promising young scientists from across the world by offering a comprehensive and challenging education for doctoral ...
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Application Deadline: September 1, 2024. The Autism, Bilingualism, Cognitive and Communicative Development (ABCCD) group in Fribourg, Switzerland, is looking for a post-doctoral senior expert in data analysis to join their team for 2 years (starting in September 2024), preferably at 50%, although this is open to discussion (42,260 chf per year salary for 50%).
PhD Programme in Data Science
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 ...
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Waterloo Data and Artificial Intelligence Institute Home. ... Learn more about the four winners of the $5,000 Waterloo.AI 2024 Graduate Scholarship ... Yudong Luo - PhD Candidate (MATH - Computer Science) Yudong's Ph.D. research focuses on Reinforcement Learning, an emerging field within AI, under the guidance of Professor Pascal Poupart. ...
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August Lamb, left, and Will Tenpas. Tenpas and Lamb both recently completed the residential master's program at the School of Data Science as well as their careers as competitive collegiate swimmers. Douglass, who finished her UVA swim career in 2023 as one of the most decorated swimmers in NCAA history, is a graduate student in statistics at the University and is set to compete in the Paris ...
Information for NEW graduate students in Actuarial Science, Data
Director of Graduate Studies, Statistics and Data Science Graduate Advisor: [email protected] (319) 335-2294. Professor N.D. Shyamalkumar. Actuarial Science Graduate Advisor: [email protected] (319) 335-1980. Margie Ebert. Academic Services Coordinator, [email protected] (319) 335-2082. Heather Roth

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