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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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Additive Manufacturing (AM) or 3D Printing (3DP) technology is fundamentally transforming manufacturing processes in various industries. However, the risks associated with less-than-ideal printing
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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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