Doctoral Candidate in reliable machine learning (100%, f/d/m)

10.07.2024, Wissenschaftliches Personal

At the chair of Data Analytics and Machine Learning at the Technical University of Munich (TUM), a full-time posi-tion will be available from September 2024 in the DFG project GeoMAR: Geometric Methods for Adversarial Robustness as a Doctoral candidate / PhD Student (f/m/d). You will be conducting research in the area of robust machine learning.

Your area of responsibility
- Research in the area of robust machine learning. Recent work of our group includes:
- Robustness of neural networks (https://proceedings.mlr.press/v162/schwinn22a.html)
- Novel threat models in LLMs (https://arxiv.org/pdf/2402.09063)
- Efficient Adversarial Training in LLMs (https://arxiv.org/abs/2405.15589)

Your profile
- University degree (M.Sc.) with very good grades in Computer Science or related fields
- Strong background in machine learning
- Strong programming skills in Python and experience with deep learning frameworks (PyTorch or similar)
- Proficient in spoken and written English; German language skills are not required

What we offer
- Salary is according to the level TV-L E 13 of the German public sector
- Work as part of a research group integrated into the chair of Prof. Stephan Günnemann (TUM)
- Close collaboration with the Professorship for Mathematics of Machine learning at the Julius-Maximillian-Universität Würzburg
- Opportunities for international collaboration

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 Dr. Leo Schwinn ([email protected]; subject: "PhD Application"). The pdf should include (i) a brief statement of interest/motivation letter and why you fit to our group (at most half a page), (ii) a curriculum vitae, (iii) copies of certificates/transcripts, (iv) a summary/abstract of the master thesis. Feel free to include in your e-mail body a very short summary of your major achievements (e.g. excellent grades, internships, papers, ...). A list of references (names, contact information) is helpful as well. Applications will be considered as they are received and until the positions are filled. Further information about the chair (https://www.cs.cit.tum.de/daml/) and myself (https://schwinnl.github.io/)

As part of the Excellence Initiative of the German federal and state governments, TUM has been pursuing the stra-tegic 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. As part of your application for a position at the Technical University of Munich (TUM), you submit personal data. Please note our data protection information in accordance with Art. 13 of the General Data Protection Regulation (GDPR) on the collection and processing of personal data in the context of your application https://portal.mytum.de/kompass/datenschutz/Bewerbung/. By submitting your application, you confirm that you have taken note of TUM's data protection information.

The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.

Data Protection Information:
When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.

Kontakt: [email protected]

https://schwinnl.github.io/