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control and grid-forming/grid-following operation; HVDC, MTDC and offshore systems; protection; machine and load modelling; model order reduction; parameter estimation and model validation; large-scale
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, uncertainty-aware parameter estimation, surrogate modeling, and reduced-order modeling where relevant. The scientific emphasis is mechanics-first: we are looking for someone with strong foundations in nonlinear
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Neural Nets (PiNNs) or “ML inspired” traffic models. PhD2 focuses on data assimilation and estimating start and boundary conditions such as path-flows, and other key parameters and inputs. In your role as
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focuses on hybrid traffic flow modelling such as Physics inspired Neural Nets (PiNNs) or “ML inspired” traffic models. PhD2 focuses on data assimilation and estimating start and boundary conditions such as