Developing machine learning methods for many-body quantum physics can help us solve generic many-body quantum models, as well as study diverse phenomena in strongly-correlated electron systems. As a PhD Candidate, you will focus on developing these machine learning models to push this emerging field forward.
We are looking for a highly motivated and skilled candidate with a strong background in theoretical physics or computer science to join us in our effort to develop machine learning methods for many-body quantum physics. Our primary goal is to push this emerging field forwards to the point where deep learning could be universally used for solving generic many-body quantum models and studying diverse phenomena in strongly correlated electron systems, such as superconductivity or spin liquids. Your research will be balanced between method development and addressing concrete physical questions. You will work as a member of the Theory of Condensed Matter group at the Institute for Molecules and Materials at Radboud University under the supervision of Dr Andrey Bagrov and Prof. Mikhail Katsnelson and in a close collaboration with other PhD candidates. You will also have some moderate teaching duties such as serving as an assistant at problem classes and grading homework (~3-4 hours per week every second term).
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