Stellenangebot
Open Phd position (75% TVL-E13, m/f/d) in Statistical Machine Learning, University of Regensburg
Wiss. Mitarbeiter*in (m/f/d) - 75% 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. At the Chair of Machine Learning at the Faculty of Informatics and Data Science, our main interest is the development and analysis of machine learning methods, especially with regard to their statistical properties. One of our main application areas of application is the analysis of high-dimensional and complex genetic data. Here we work closely with the Genetic Epidemiology Unit at the University of Regensburg. A central aspect of our work is the combination of application orientation and theoretical foundation. We conduct rigorous theoretical investigations of the respective methods, which creates a deep mathematical understanding. This understanding forms the basis for the targeted development of new machine learning algorithms.
The Chair of Machine Learning invites applications for the position of a
Doctoral Researcher (Ph.D. candidate) (m/f/d) part-time
to start at the earliest convenience. This is a part-time position (30,075 hours per week), based on a fixed-term contract valid for a period of 3 years (§ 2 Abs. 1 WissZeitVG).
The official listing with more information on the position can be found here:
German: https://www.uni-regensburg.de/assets/universitaet/stellenausschreibungen/fuer_Forschung_und_Lehre/23_258_FIDS_WissMA.pdf
English: https://www.uni-regensburg.de/assets/informatics-data-science/machine-learning-behr/23.258_FakFIDS_WissMA_eng.pdf
If you have any questions, please contact Prof. Merle Behr via e-mail:
merle.behr@informatik.uni-regensburg.de.
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 merle.behr@informatik.uni-regensburg.de by November 25, 2023.
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. At the Chair of Machine Learning at the Faculty of Informatics and Data Science, our main interest is the development and analysis of machine learning methods, especially with regard to their statistical properties. One of our main application areas of application is the analysis of high-dimensional and complex genetic data. Here we work closely with the Genetic Epidemiology Unit at the University of Regensburg. A central aspect of our work is the combination of application orientation and theoretical foundation. We conduct rigorous theoretical investigations of the respective methods, which creates a deep mathematical understanding. This understanding forms the basis for the targeted development of new machine learning algorithms.
The Chair of Machine Learning invites applications for the position of a
Doctoral Researcher (Ph.D. candidate) (m/f/d) part-time
to start at the earliest convenience. This is a part-time position (30,075 hours per week), based on a fixed-term contract valid for a period of 3 years (§ 2 Abs. 1 WissZeitVG).
The official listing with more information on the position can be found here:
German: https://www.uni-regensburg.de/assets/universitaet/stellenausschreibungen/fuer_Forschung_und_Lehre/23_258_FIDS_WissMA.pdf
English: https://www.uni-regensburg.de/assets/informatics-data-science/machine-learning-behr/23.258_FakFIDS_WissMA_eng.pdf
If you have any questions, please contact Prof. Merle Behr via e-mail:
merle.behr@informatik.uni-regensburg.de.
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 merle.behr@informatik.uni-regensburg.de by November 25, 2023.