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computational methods for the control, optimization, and coordination of complex dynamical systems. The work will emphasize applications to multi-agent systems, including robotics, autonomous systems, and
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods
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characterization, and computational modeling. Prior experience with solid-state NMR spectroscopy, including magic-angle spinning experiments, multinuclear NMR, quadrupolar nuclei, or two-dimensional NMR methods, is
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems Requirements: PhD in Mathematics, Computer Science
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magic-angle spinning experiments, multinuclear NMR, quadrupolar nuclei, or two-dimensional NMR methods, is highly desirable. Experience with computational methods such as density functional theory
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custom experimental systems. These instruments may combine optical components, laser and spectroscopic methods, spin-control or magnetic-resonance techniques, electronics, data-acquisition hardware, and
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-efficiency to realized efficient Embodied/Edge-AI implementations. A key focus will be on investigating novel methods with strong theoretical foundations as well as their full-system deployment in real-world
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results of the state-of-the-art (SOTA) methods; Design and develop experimental facilities to allow implementing experiments on real platforms operating indoors and outdoors, to compare our proposed work
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on investigating novel methods with strong theoretical foundations as well as their full-system deployment in real-world applications from different fields (like autonomous systems, healthcare, and robotics) with
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Experience in machine-learning modeling for solid mechanics applications Experience in the development and coupling of numerical methods for solid mechanics modeling Experience in digital rock technology