Ph.D. / Postdoc candidate (100% TVL-E13, m/f/d), Faculty of Informatics and Data Science

at Universität Regensburg
Published November 12, 2022
Location Regensburg, Germany
Category Machine Learning  
Job Type Scholarship  



Wiss. Mit­ar­bei­ter*in (m/f/d) - 100% Arbeitszeit - E13 TV-L
Fakultät für Informatik und Data Science
Universität Regensburg

The University of Regensburg with its more than 20,000 students is an innovative and interdisciplinary oriented campus university with a broad range of academic disciplines and research activities for young people from Germany and abroad. The Chair of Machine Learning is a currently growing group at the newly established Faculty of Informatics and Data Science.

In recent years, new complex data has become available in many areas. At the Chair of Machine Learning we are interested, for example, in high-dimensional omics data and large databases of electronic health records (Big Data), where we work within various collaborations. Using machine learning (ML) methods, our chair enables the linking and analysis of such data, which can lead to fundamental new insights. Our focus is on statistical machine learning. Thus, a central part of our application driven work is also the rigorous theoretical investigation of the respective methods. The resulting mathematical understanding often enables the targeted development of new ML algorithms.

The Chair of Machine Learning invites applications for the position of a

Doctoral Researcher (Ph.D. candidate) or Postdoctoral Researcher (Postdoc) (m/f/d)

to start at the earliest convenience. This is a full-time position (40,1 hours per week), based on a fixed-term contract valid for a period of 3 years (§ 2 Abs. 1 WissZeitVG). Remuneration is in accordance with pay grade TV-L E13.

The official listing with more information on the position can be found here:



If you have any questions, please contact Prof. Merle Behr via e-mail:
We look forward to receiving your detailed application (including curriculum vitae, cover letter, relevant certificates, and contact details of reviewers), which should be sent in a single PDF file by e-mail to by December 15, 2022.