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algebras, quantum affine algebras, algebraic Lie Theory, number theoretical aspects of representation theory, structure theory of Kac-Moody algebras, geometric/combinatorial representation theory, quiver
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12.05.2025, Wissenschaftliches Personal PostDoc- und Promotionsstellen in der Theorie der Quantensimulation Dauer: 2 - 3 Jahre (PostDoc) & 3 - 4 Jahre (Promotion) Ort: School of Natural
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interdisciplinary team. Applicants with strong background in the following fields are preferred: Dynamical Systems Control Theory Formal Methods Machine Learning Context The applicant will be directly advised by Prof
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research and take over a leadership role (Team Lead) in the institute Motivation Do you want to put your scientific career on the fast track and feel electrified? Do you have ambitions to lead a research
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: - QUANTITATIVE VERIFICATION: analysis of probabilistic systems (Markov decision processes, stochastic games, chemical reaction networks), automata theory and temporal logic, machine learning in verification
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06.10.2022, Wissenschaftliches Personal We synthesize and test novel catalysts for sustainable energy conversion processes such as polymer electrolyte fuel cells or electrolyzers, H2O2 production, or electrochemical CO2 reduction. To do so, the preparation of novel catalyst materials is of...
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: Dynamical Systems Control Theory Formal Methods Reachability Analysis Computational Geometry Context The applicant will be directly advised by Prof. Matthias Althoff (https://www.ce.cit.tum.de/cps/members
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research and knowledge transfer Independent, creative and committed way of working Ability to think conceptually and analytically Excellent English skills Ideally, good knowledge of German Our offer: We
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communication system are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random
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object interaction (using environments such as MuJoCo, PyBullet, or NVIDIA Isaac Sim) Adaptive feedback control using learned and analytical models Physics-informed machine learning for deformation