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(MPC) and Reinforcement Learning (RL) have proven effective in isolated studies, their widespread deployment is hindered by the lack of interoperability, the high cost of model creation, and data
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If you want to pursue a research career at the intersection of additive manufacturing (AM), microstructural engineering and advanced statistical/machine-learning (ML) based modelling, then this PhD
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-physics modelling of power electronic systems and components, with special focus of magnetic components, Incorporating physics-driven machine learning approaches in power electronics design, Incorporating
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infrastructure. The research will investigate how machine learning models can be designed and deployed efficiently on constrained hardware platforms while supporting the reliability and security requirements
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Time Series Analysis Graphical models Computational statistics We welcome applications from candidates with an educational background in fields such as: Mathematical Statistics Machine Learning