Tristan Bereau
4 days ago
Fully funded PhD in generative machine learning for molecular thin films Heidelberg University in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
Full funding available
Country
Germany
University
Heidelberg University
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About this position
PhD opportunity in generative machine learning for molecular thin films at the Institute for Theoretical Physics, Heidelberg University, in the group of Prof. Tristan Bereau, with a joint collaboration with Prof. Ullrich Köthe.
The project focuses on developing generative machine-learning methods for amorphous molecular thin films, which are important supramolecular structures for organic-electronic materials. The research combines statistical mechanics, molecular simulation, and deep generative modeling, with an emphasis on diffusion models, coarse-grained representations, and free-energy estimation.
The successful candidate should have a strong background in statistical mechanics, experience in machine learning and/or molecular simulation, and strong Python and PyTorch skills. A genuine interest in method development and comfort with mathematical formalism are important. A physics degree is required for admission to the Heidelberg Graduate School for Physics.
The position is fully funded for three years at 75% E13 TV-L. There is no application deadline; applications are reviewed on a rolling basis until the position is filled.
To apply, send a CV, a short statement of interest, and the names of two referees to bereau@thphys.uni-heidelberg.de.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
How to apply
Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.
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