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will be developed using the OpenFOAM toolkit. A modelling workflow will be created, and then used as the basis for the optimisation, potentially using tools such as Bayesian Optimisation. In addition
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Bayesian inference framework for identifying complex aerospace systems combining with limited experimental data. It can be also used to quantify uncertainties from experimental testing, significantly
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optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
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Processing/Control Path Planning/Trajectory Planning Multi-Target Tracking/Multi-Object Tracking, Bayesian Filtering, Radom Finite Set filters or closely related multi-target tracking approaches in radar
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, adversarial attacks, and Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering