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, and unmodeled dynamics remains a key challenge. This position focuses on developing and validating methods that jointly address safety, performance, and reliability of learning-based control and
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development of digital quantum algorithms for the simulation of non-abelian lattice gauge theories. We are looking for highly motivated individuals, with the desire to perform theoretical physics research
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to guarantee user-defined error bounds of reachable sets for nonlinear and hybrid systems. This project will exactly close this research gap: We will develop essentially new methods to ensure that algorithmic
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of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning
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complex multiphysics problems. The coupling is done across package boundaries. This also requires more sophisticated approaches in load-balancing. Finally, the newly developed algorithms will be tested and