PhD Student in Physics of Molecular and Biological Matter – Machine Learning for X-ray and Neutron Scattering Data Analysis University of Tübingen in Germany

The Physics of Molecular and Biological Matter group at the University of Tübingen, led by Prof. Dr. Dr. h.c. Frank Schreiber, is seeking a motivated PhD student to join their interdisciplinary research team. The group specializes in the physics of molecular and biological matter, employing advanced X-ray and neutron scattering techniques. A key focus is the development and application of machine learning (ML) strategies for efficient analysis of large datasets generated from scattering experiments.

This position offers a unique opportunity to contribute to cutting-edge research at the intersection of physics, chemistry, and computer science. The successful candidate will develop ML-based tools for analyzing data from surface sensitive scattering techniques such as X-ray Reflectivity (XRR) and Grazing-Incidence Wide-Angle X-ray Scattering (GIWAXS). Responsibilities include supporting data and metadata formats, integrating software into computational environments, and presenting scientific results at conferences and in publications.

The group is part of the Institute of Applied Physics and collaborates with large national and European research consortia, including the DAPHNE NFDI consortium. Students benefit from well-equipped laboratories, a highly collaborative international environment, and membership in the DFG-funded Cluster of Excellence "Machine Learning: New Perspectives for Science." Research activities may involve participation in measurement campaigns at synchrotron and neutron facilities, providing practical experience and opportunities for professional development.

Applicants should hold a Master’s degree in physics, chemistry, computer science, or a related field. Essential qualifications include strong interest in physics and machine learning, good written and spoken English, and the ability to work both independently and collaboratively. Programming skills in Python and familiarity with ML frameworks such as PyTorch or JAX are highly advantageous. Experience with surface sensitive scattering techniques and knowledge of German are considered pluses.

The University of Tübingen is renowned for its academic excellence, vibrant student life, and commitment to equal opportunities and diversity. The position is funded at E13 TV-L, 50-75% for three years, with access to international research facilities and excellent training and supervision. Applications should include a cover letter, CV, and transcript of records, submitted as a single PDF file to [email protected]. The application deadline is 31.07.2026, and the position is available immediately.

For further information about the group and research activities, visit www.soft-matter.uni-tuebingen.de. For details on the application process, see the official job advertisement at this link.