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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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, you will leverage the power of graph neural networks – a novel ML architecture, capable of learning fundamental physical behaviour by modeling systems as graphs and encoding nonlinearities in these. As
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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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