The Machine Learning research group at the University of Oldenburg is seeking to fill a position for a
(wissenschaftliche Mitarbeiterin / wissenschaftlicher Mitarbeiter
E13 TV-L, 75%).
The position can be filled immediately and is funded until 31st December 2018. We intend to continue the funding of the position thereafter.
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 on Machine Learning and Computational Neuroscience including many projects in the Cluster of Excellence Hearing4all. We pursue basic research, develop new technology, and apply our approaches to different tasks such as acoustic, visual and medical data analysis. Our research combines modern probabilistic approaches, modern computer technology and insights from the neurosciences. We develop novel methods and improve existing methods for computer hearing, computer vision, 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 new probabilistic algorithms for structure finding in data, including component extraction, clustering and analysis with deep probabilistic models. The data the developed algorithms are applied to will include acoustic and/or medical data. The research work includes basic research and practical algorithm implementations. The position is funded by the DFG in the Cluster of Excellence Hearing4all.
Applicants have to hold an academic university degree (Master or equivalent) in Physics, Mathematics, Computer Science, Electrical Engineering or a closely related subject; or they have to be close to obtaining their MSc degree (such that they have the degree when they start the position). Strong analytical/mathematical skills, e.g. as obtained in theoretical/mathematical courses of a Mathematics degree, are required for all candidates. Furthermore, good programming skills (e.g. matlab, python, C++) and prior experience with probabilistic Machine Learning approaches are required. Good English language skills are required and German language skills are very desirable.
The appointed researcher will be part of a very dynamic working environment in a research group that represents an expanding new research domain. The group is located in a new building, and the Cluster of Excellence Hearing4all is part of the German Excellence Initiative which funds top-tier research in Germany.
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 cases of equal qualification. Handicapped applicants will be given preference if equally qualified.
Please send your application preferably electronically (PDF) to Imke Brumund (email@example.com) or per mail to: Carl von Ossietzky Universität Oldenburg, Fakultät VI, Machine Learning, z.Hd. Frau Imke Brumund, 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 university degrees (or a preliminary transcript if applicable), publications if applicable, and one recommendation letter or contact details of two of your past/current advisors). Please use "Research Associate Position, Machine Learning" as subject line.
Please send your application until 31st July 2017.
Universitätseigene Stellenausschreibungen werden ausschließlich erst nach Bestätigung durch die/den jeweilige(n) PersonalsachbearbeiterIn des Dezernates 1 und anschließender Weiterleitung hierher aufgenommen.