Professorship in Machine Learning in Physics

Professorship in Machine Learning in Physics

University of Tübingen

Tübingen, Germany

The professorship is embedded within the Excellence Cluster "Machine Learning: New Perspectives for Science", which is now entering its second funding phase. The successful candidate is expected to have an established research profile in a core area of physics, as well as a strong track record in research questions related to machine learning and/or artificial intelligence.

Core areas of physics include the structure of condensed matter (description of many-body systems), quantum physics (characterization of quantum states in many-body systems), and theoretical particle physics. In all of these areas, methodological development through machine learning is taking place — for example, in predicting the evolution of complex molecular systems over long timescales, investigating quantum mechanical effects in information processing within (quantum) neural networks, or in elementary particle physics at high-energy accelerators.

The aim of the professorship is to closely integrate research at one of the Department of Physics’ research centers (BioNanoPhysics Center, Center for Quantum Science, or Kepler Center) with ongoing machine learning research activities in Tübingen. The appointee is expected to be actively involved in the Department of Physics and in the Excellence Cluster, inter alia by pursuing collaborative research projects at the interface of machine learning and physics, as well as by committing to organizational and implementation-related tasks within the Excellence Cluster.

In terms of teaching, the professorship is expected to offer courses within the Department of Physics and also contribute to the international Master's program “Machine Learning” offered by the Department of Computer Science.

Required qualifications include a PhD or equivalent degree as well as postdoctoral qualifications and teaching experience equivalent to the requirements of a full professorship.