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 are accepted into the program through either one of the following tracks:
- Application to an open call once per year, see deadlines below.
- Application to a faculty member of a participating research group. These applications are considered on a rolling basis.
Decisions regarding enrollment into the program will be made by an admission committee. The PhD program in Data Science is usually 3-4 years long.
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
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.
- Introduction to Data Science
In this course, students will learn the basic tool kit of a data scientist, such as regression and classification algorithms, Bayesian statistics, and machine learning. Students will also have the opportunity to apply their knowledge on a 6 weeks long project on a data science topic of their choice.
- 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:
PDF (DE) (PDF, 107 KB)
Admissions to Promotion - Useful links and documents:
Prof. Dr. Jan Dirk Wegner