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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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interest. They include but are not limited to 1) innovative use of AI/ML for designing and optimizing photonic materials, devices, and systems, 2) enabling photonic technologies for AI computing and
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to: - Developing underwater communication systems using deep learning which are well-performing to nonlinear channels. - Establishing a deep learning architecture which is optimal for underwater acoustic
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, including being well-versed in a variety of nonlinear editing systems Knowledge of Microsoft Office, Google Suite, PowerPoint, working with laptop and projectors' basic knowledge. Familiarity of online
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, feedback optimization, distributed control, consensus protocols, nonlinear control, robust control, with application to energy systems (e.g. smart grids, district heating, hydrogen networks) and traffic
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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