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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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to precisely shift microbiomes to desired metabolic states. Our research combines multiplexed measurements of single cells, populations and ecosystems with concepts from nonlinear dynamical systems, control
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chargers, or nonlinear loads Experience in hardware-in-the-loop testbeds and digital twin creation Experience with SEL RTAC 3555 or similar Experience in advanced microgrid controls such as nonlinear methods
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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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needed 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