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The Machine Learning research lab at the University of Oldenburg is seeking to fill a position for a

Research Associate (PhD) (m/f/d)
(wissenschaftliche Mitarbeiterin / wissenschaftlicher Mitarbeiter, E13 TV-L, 75 %).

The position can be filled immediately and is funded for three years. Depending on the state of the obtained research results, an extension can be considered.

The person who fills the position will be part of the Machine Learning group which develops learning and inference algorithms for different types of data. The group is actively involved in a number of projects which aim at developing novel Machine Learning approaches for challenging data. We pursue basic research, develop new technology, and apply our approaches to different tasks such as visual, acoustic and medical data analysis. Our research combines modern probabilistic approaches, modern computer technology and insights from the neurosciences. We develop novel approaches and improve existing methods for computer vision, computer hearing, medical diagnostics, and general pattern recognition. Furthermore, we model biological information processing and use the obtained insights to contribute to the development of artificial intelligence. Research will be conducted in close collaboration with leading international and national labs. Our Machine Learning research can be considered as part of the Data Sciences, Computational Sciences, or Big Data approaches.

The research focus of the position will be on the development of novel algorithms that are based on probabilistic Machine Learning approaches. Applications of such approaches, which include deep generative models, are for instance enhancement of data under difficult conditions, unsupervised and semi-supervised learning, structure finding and recognition. The research work emphasizes novel theoretical developments and includes applications, e.g., to visual, medical or acoustic data. The holder of the position is expected to work towards a PhD/doctoral degree.

The appointed researcher will be part of a very dynamic working environment in a research group that represents an expanding new research domain.

For more information about the research group visit

Applicants have to hold an academic university degree (e.g. Master) in Physics, Mathematics, Computer Science, Electrical Engineering Data Sciences or a closely related subject. Instead of already holding the degree candidates can be close to obtaining their academic university degree (such that they hold the degree when they start the position). Strong analytical/mathematical skills, e.g. as obtained in theoretical/mathematical courses of a Mathematics, Physics or Data Science degree, are required for all candidates. Furthermore, good programming skills (e.g. python, C++, matlab) and prior experience with probabilistic Machine Learning approaches are required. Good English language skills are required and German language skills are desirable.

The University of Oldenburg is dedicated to increasing the percentage of women in science. Therefore, female candidates are particularly encouraged to apply. According to § 21 III NHG (legislation governing Higher Education in Lower Saxony) preference will be given to female candidates in case of equal qualification. Applicants with disabilities will be given preference if equally qualified.

Please send your application preferably electronically (PDF) to Jörg Lücke or per mail to Carl von Ossietzky Universität Oldenburg, Fakultät VI, Machine Learning, z. Hd. Prof. Jörg Lücke, 26111 Oldenburg, Germany. The application documents should contain: a short cover letter stating why you are interested in the position (half a page), a CV, transcripts of BSc and MSc degrees (or a preliminary transcript if applicable), publications if applicable, and one recommendation letter. Furthermore, please state the contact details of two of your past/current advisors (one advisor can be the same person who has written the letter). Please use "Application Research Associate Position 2020" as subject line.

Please send your application until 19 July 2020.


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