Technische Universität München

PhD and Postdoc Positions in the Area of Machine Learning (e.g. Adversarial ML, Transfer Learning, ML for Graphs and Sequences)

 
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PhD and Postdoc Positions in the Area of Machine Learning (e.g. Adversarial ML, Transfer Learning, ML for Graphs and Sequences)

18.10.2019, Wissenschaftliches Personal

The Department of Informatics (https://www.?url=in.tum.de&module=jobs&id=151012" target="_blank" rel="nofollow">in.tum.de) at Technische Universität München (TUM) invites applications for PhD/researcher positions at the research group Data Analytics and Machine Learning (www.?url=kdd.in.tum.de&module=jobs&id=151012" target="_blank" rel="nofollow">kdd.in.tum.de).

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:

- Machine learning for graphs / geometric deep learning
- Robust and adversarial machine learning
- Transfer learning
- ML models for temporal data (e.g. event sequences)
- Anomaly and outlier detection
- Uncertainty in ML (e.g. Bayesian neural nets)

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 following venues: ICML, KDD, NeurIPS, ICLR, or WWW)
- Strong background in machine learning / data mining
- Strong programming skills in at least one programming language (preferably Python and with experience in TensorFlow, PyTorch or similar)
- 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; no links to external files; in English or German) by email to Prof. Dr. Stephan Günnemann (guennemann [at] in[.]tum[.]de; 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. A list of references (names, contact information) is helpful as well.

Salary is according to the level TV-L E 13 of the German public sector (for PostDocs: option of E 14). 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.

Applications will be considered as they are received and until the positions are filled.
Please check our website for up to date information: ?url=http%3A%2F%2Fwww.kdd.in.tum.de%2Fopen-positions%2F&module=jobs&id=151012" target="_blank" rel="nofollow">www.kdd.in.tum.de/open-positions/
For further information, please do not hesitate to contact Prof. Stephan Günnemann (guennemann [at] in[.]tum[.]de).

Hinweis zum Datenschutz:
Im Rahmen Ihrer Bewerbung um eine Stelle an der Technischen Universität München (TUM) übermitteln Sie personenbezogene Daten. Beachten Sie bitte hierzu unsere Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. Durch die Übermittlung Ihrer Bewerbung bestätigen Sie, dass Sie die Datenschutzhinweise der TUM zur Kenntnis genommen haben.

Kontakt: guennemann [at] in[.]tum[.]de

Mehr Information

?url=http%3A%2F%2Fwww.kdd.in.tum.de%2Foffene-stellen%2Fhiring%2F&module=jobs&id=151012" target="_blank" rel="nofollow">www.kdd.in.tum.de/offene-stellen/hiring/

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