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to enhance our multidisciplinary research at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement
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optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques to quell opto-mechanical
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on reliability, security, and resilience of microgrid systems. This includes advanced control system design, modeling and analysis, control architecture design and communication-based control and optimization
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the Research Group Nonlinear Optimization and Inverse Problems (Head: Prof. Dr. D. Hömberg) starting as soon as possible. The position is within the Math+ project "Anisotropic microfluids -- fluctuations