PhD and PostDoc Positions (Wissenschaftliche/r Mitarbeiter/in)

PhD and PostDoc Positions (Wissenschaftliche/r Mitarbeiter/in) in the area of Data Mining, Data Science, Machine Learning

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 data mining/machine learning methods. Particularly, the focus of the research work will be on large-scale graph mining techniques and temporal data analysis. The developed methods will be applied and evaluated in various domains such as the life sciences (e.g., protein interaction networks) and the web (e.g., community and fraud detection in social networks).

** Candidate skills & profile **

  • University degree (M.Sc. or Diploma) with very good grades in Computer Science, Mathematics, Statistics, or equivalent (For PostDocs: Ph.D. in the corresponding areas)
  • Strong background in data mining, machine learning, big data analytics, or similar topics
  • Strong programming skills in at least one programming language (Java, C++, Python, R, etc.)
  • 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 one file in pdf format; in English or German) by email to Prof. Dr. Stephan Günnemann (guennemann@in.tum.de; subject: Application PhD). The application should include a brief statement of interests/motivation letter, a curriculum vitae, copies of certificates, a summary of the master thesis, names of references, and (if already available) a list of publications. Applications will be considered as they are received and until the position is 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 (guennemann@in.tum.de).