Open positions & Student Projects/Thesis
PhD Students and Postdoctoral Fellows
We continuously seek talented and ambitious scientists interested in unique interdisciplinary research, integrating molecular simulations, machine learning, statistical physics, multiscale modeling, and uncertainty quantification. If you are interested, please send your application to info.mmfm(at)mw.tum.de (prof. Zavadlav) with the subject “Ph.D. position” or “PostDoc position”, respectively. The application should include a cover letter (motivation to join our group, how your previous work/knowledge/interest relates to our research topics and publications), CV, transcript of grades, desired starting date, and, if applicable, publication list. If possible, provide evidence of your programming skills (e.g., GitHub repos). Our team is international; please apply in English.
Semester/Bachelor/Master Thesis and Research Internships
Thesis / Research internship opportunities are always available. The topic can be tailored to the student's interests but within the group's research area. The objectives of projects can be adjusted to fit a research internship, an IDP, a Semester, a Bachelor's, or a Master's thesis. Day-to-day supervision will be provided by one of the PhD students or postdocs working on the topic. If you are interested, please email the respective supervisor and provide a brief introduction of yourself (motivation, background, CV) and the transcript of records of your Bachelor's and Master's program.
Open Theses and HiWi-Positions can be found in the Theses & HiWi Jobs database of the FSMB:
Interdisciplinary Projects (IDPs) for Informatics
The topic can be tailored to the student's interests but within the group's research area. If you are interested, please email the respective supervisor and provide a brief introduction of yourself (motivation, background, CV) and the transcript of records of your Bachelor's and Master's program.
Currently available topics:
- Active Learning-Driven Development of Machine Learning Potentials for Molecular Self-Assembly Dynamics
- Developing a Multi-Element Neural Network Potential for the Simulation of Aluminum Alloys
- Deep Generative Models for Enhancing Molecular Simulations