Mario Krenn

The University of Tübingen is offering several fully-funded PhD and Postdoc positions in the field of Artificial Intelligence for Scientific Discovery in Physics. The positions are part of the Machine Learning in Science group led by Mario Krenn, and are supported by the Cluster of Excellence (Machine Learning for Science), the ERC Starting Grant ArtDisQ, and the university itself. Research topics include developing modern auto-differentiation physics simulators, AI-driven discovery of hardware for quantum information technology, quantum-enhanced microscopes and telescopes, and new physics experiments to test quantum-gravity and observe gravitational waves. The group also works on state-of-the-art AI-driven exploration, optimization, and search algorithms in complex spaces motivated by physics and chemistry, as well as agentic frameworks for closed-loop idea generation in physics. The group values interdisciplinary approaches, including insights from the philosophy of science. PhD positions are for 3 years and require a Master’s or Bachelor’s degree in Physics, Computer Science, or related fields. Postdoc positions are for 2 years and require a Doctorate in relevant fields. The salary is based on the German public service wage agreement (E 13 TV-L). The university encourages applications from qualified women scientists. To apply, candidates should send a CV, a short motivation letter, and contact details of two references to mario.krenn@uni-tuebingen.de. The application deadline is October 28, 2025, or until the positions are filled.

Up to 3 PhD positions are open in the interdisciplinary EXPRESSO project, part of the Tübingen Excellence Cluster Machine Learning: New Perspectives for Science at the University of Tübingen. The project focuses on quantum physics and machine learning , with research topics including probabilistic simulation and inference for quantum gases, simulation-guided model discovery via adaptive amortized inference, and representation learning for optimization in exponential search spaces. Supervision is shared across physics and machine learning by Philipp Hennig, Mario Krenn, Igor Lesanovsky, Jakob Macke, and Georg Martius. The positions are tightly integrated and designed for candidates interested in bridging quantum science, probabilistic inference, deep learning, and optimization. Applicants should have a Master’s degree in Physics, Computer Science, Mathematics, or a related field, plus strong mathematical and programming skills. Experience in quantum mechanics, Bayesian methods, or deep learning is an advantage. The team also values interdisciplinary motivation and the ability to build concrete software artifacts. Funding is a paid PhD appointment at TV-L E13, 75% for 3 years. Applications are reviewed on a rolling basis, and early applications are strongly encouraged. The review period closes on 14 September 2026. To apply, submit a CV, a short motivation letter, and the names of two referees via the application form, and indicate your preferred project(s).