Ph.D. position in Data-Driven Molecular Material Design (100% E13)

12.03.2025, Wissenschaftliches Personal

By integrating state-of-the-art machine learning models (graph neural networks, diffusion models) with quantum chemistry and molecular simulations, the project aims to accelerate bottom-up material discovery for applications ranging from life sciences to engineering. For more information, visit our webpage www.epc.ed.tum.de/en/mfm.

Your profile
- M.Sc. degree in chemistry, physics, or informatics (candidates that will soon obtain the degree are also welcome to apply)
- strong background in machine learning
- proficiency in programming (especially Python)
- experience with ab initio/molecular simulations and knowledge of statistical physics is beneficial
- fluent in spoken and written English (knowledge of German is not required)

Our offer
You will join a young research group working on state-of-the-art research in molecular modeling and become part of TUM, a top European university. The position is available immediately and for three years. Salary is based on the Free State of Bavaria public service wage agreement (100%, TV-L E13). Additional funding is available for computational equipment and conference travel expenses.

How to apply?
Please send your application in English by e-mail to info.mmfm@mw.tum.de with the subject “PhD Application”. The application should include (one PDF document) a cover letter (motivation to join our group, how your previous work/knowledge/interest relates to our research topics and publications), a CV, a grades transcript, two references' contact information, and a desired starting date. Provide evidence of your programming skills (e.g., GitHub repository) if possible. Applications will be reviewed on a rolling basis until the position is filled. Preference will be given to applications received before 1 May 2025.

If you have any questions, please do not hesitate to contact Prof. Dr. Julija Zavadlav (info.mmfm@mw.tum.de).

Kontakt: info.mmfm@mw.tum.de

Mehr Information

https://www.epc.ed.tum.de/en/mfm