PhD Programme in Data Science

Graduate School for Data Science

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

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

Admissions

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.

Graduation

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

PhD students in the Data Science Programme must complete a curricular component of at least 12 ECTS credits. These credits can be earned through a combination of specialized courses, transferable skills training, and research-related activities.

Coursework Options:

Students can choose courses from the Department of Mathematics and Computer Science, the Faculty of Science, or other relevant faculties at UZH and ETH Zurich.
Suitable courses may cover topics such as machine learning, statistical modelling, optimization, and computational methods.
Participation in doctoral schools, summer schools, and workshops may also be credited, subject to approval.

Coursework Milestone & Documentation:

To validate the Coursework Milestone, students must upload supporting documents to the system:

For courses booked in a regular manner via the booking tool, by a Modulverantwortliche/-r or PhD coordinator, students should upload the transcript of records.

For all other cases, students can fill out a document listing the course names and ECTS credits and must have it signed by the head of the doctoral committee before submission.

Approval and Registration:

Students should select their courses in consultation with their supervisor and doctoral committee to ensure they align with their research focus.
Course registration follows the standard procedures outlined by UZH and ETH Zurich.
External courses and summer schools may require prior approval to be counted toward the 12 ECTS credits.

For more information on course offerings, visit the UZH Course Catalogue and the ETH Zurich Course Catalogue.

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.