PhD and PostDoc Positions (Wissenschaftliche/r Mitarbeiter/in)
PhD and PostDoc Positions (Wissenschaftliche/r Mitarbeiter/in) in the area of Machine Learning and Data Analytics.
We are looking for talented and highly motivated computer scientists (or people with a related background) interested in the design, development, and analysis of novel machine learning methods. Particularly, we are currently offering positions focusing on the following topics:
- robust and adversarial machine learning
- transfer learning
- anomaly and outlier detection in event and sequence data
The developed methods will be applied and evaluated in various domains such as the natural sciences (e.g., molecular graphs), the field of engineering (sensor and diagnosis signals), and the web (e.g., social networks, knowledge graphs).
** Candidate skills & profile **
- University degree (M.Sc.) with very good grades in Computer Science or related fields (For PostDocs: Ph.D. in the corresponding area and publications at the relevant top-tier venues)
- Strong background in machine learning / data mining
- Strong programming skills in at least one programming language (preferably Python)
- Good English language skills (your responsibilities include to write publications and to give international presentations)
- Knowledge of German is an asset, but not a must (e.g. participation in national conferences)
** How to apply? **
Please send your application (in a single file in pdf format; in English or German) by email to Prof. Dr. Stephan Günnemann (firstname.lastname@example.org; subject: PhD Application). The application should include a brief statement of interests/motivation letter, a curriculum vitae, copies of certificates, a summary/abstract of the master thesis, and (if already available) a list of publications. Applications will be considered as they are received and until the positions are filled.
Salary is according to the level TV-L E 13 of the German public sector. As part of the Excellence Initiative of the German federal and state governments, TUM has been pursuing the strategic goal of substantially increasing the diversity of its faculty. As an equal opportunity and affirmative action employer, TUM explicitly encourages nominations of and applications from women as well as from all others who would bring additional diversity dimensions to the university’s research and teaching strategies. Preference will be given to disabled candidates with essentially the same qualifications.
For further information, please do not hesitate to contact Prof. Dr. Stephan Günnemann (email@example.com).