Professur (W 2) für Data Science in Sports and Health in Marburg

Ausschreibungs-ID: fb21-0016-Data-Sciense-2026 Entry date: 2028-04-01 Application deadline: 2026-11-08 Salary: W 2 HBesG Duration: permanent Volume of employment: full-time The University of Marburg, founded in 1527, offers a variety of excellent programs of study for around 22,000 students and confronts the important topics of our time through excellent research across a broad spectrum of sciences.

At the Department of Educational Science and Sports Science, Institute of Sport Science and Motology, in cooperation with the Department of Mathematics and Computer Science, the following position is to be filled at 1 April 2028 Professorship (W 2) in Data Science in Sports and Health.

Tasks

The professorship represents the field of Data Science in Sports and Health in both research and teaching. It is conceived as a methodologically oriented cross-sectional professorship within Sport Science and Motology and is intended to ensure the sustainable integration of data science methodologies into the discipline. The focus lies on the development and application of innovative approaches in statistics, machine learning, and artificial intelligence for data-driven decision support in the context of sports, movement, health, and medicine.

The successful candidate is expected to pursue at least one of the following focal areas:

Focal Areas

• Development and application of advanced methods for pattern recognition in multimodal movement and health data (e.g. data derived from wearables, video, imaging, questionnaires, interviews, and clinical sources)
• Development and implementation of simulation and predictive models in sport, physical activity, and health, including preventive, rehabilitative and medical care contexts, as well as applications involving augmented reality and visual analytics
• Development and application of edge and wearable computing technologies, encompassing real-time data analytics, algorithmic efficiency, and the validation of non-invasive measurement systems

Profile

• Applicants must hold a completed university degree in sport science, mathematics, computer science, or a closely related discipline, have completed an outstanding doctoral degree, and demonstrate exceptional teaching aptitude.
• In addition, applicants are expected to have an internationally recognized expertise in data science, statistics, machine learning, or artificial intelligence, with a clear and documented focus on sport, physical activity, health, and medicine. This expertise should be evidenced by relevant international peer-reviewed publications and conference contributions.
• The successful candidate is expected to have a proven track record in securing competitive third-party funding, substantial experience in university-level teaching, and a strong record of interdisciplinary research and collaboration.
• Desirable qualifications include alignment with at least one research priority of the Department of Educational Science and Sports Science and one profile area of the Marburg University, as well as a demonstrated commitment to mentoring early career researchers. We also welcome active engagement in interdisciplinary and inter-institutional research consortia and a willingness to contribute to academic self-governance. Furthermore, the candidate should contribute strategically to the ongoing development of all degree programs within the Institute of Sport Science and Motology and be prepared to teach in English in both participating departments.

The successful candidate will be expected to teach in German after a maximum of two years of employment.

The employment requirements pursuant to §§67 and68 of the HessHG.

Marburg University places particular emphasis on intensive supervision of students and doctoral researchers and expects faculty members to maintain a strong and active presence on campus.

Contact for more Information

Prof. Dr. Matthias Hoppe +496421-2823955 matthias.hoppe@uni-marburg.de

We support women and strongly encourage them to apply. In areas where women are underrepresented, female applicants will be preferred in case of equal qualifications.

As a certified family-friendly university, we support our employees in balancing family and career. A reduction of working time is possible.

Applicants with a disability as described in the Social Security Code (Section 2(2) and (3) of Book IX of the Social Security Code (SGB IX)) will be preferred in case of equal qualifications.

Application and interview costs can not be refunded.