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and interaction between devices have a significant impact. In this project you will work on and develop numerical modelling capabilities for arrays of turbines with non-homogeneous flows and wake
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workflows rely heavily on geometric de-featuring, an expert-driven, manual, and time-consuming process used to simplify CAD models so that meshing tools can cope with small-scale features such as fillets and
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expertise guides model development, improving accuracy and trust. Ethical considerations, including data privacy and collaboration with hospital ethics boards, are central to the project. Ultimately
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, an expert-driven, manual, and time-consuming process used to simplify CAD models so that meshing tools can cope with small-scale features such as fillets and manufacturing details. This de-featuring is not
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behaviour across multiple physical models. As the PhD researcher on this project, you will work at the intersection of machine learning, geometry processing and industrial simulation. You will have the
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, enabling early detection of conditions and personalised treatment planning. A human-in-the-loop (HITL) approach will ensure clinical expertise guides model development, improving accuracy and trust. Ethical
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physical models. As the PhD researcher on this project, you will work at the intersection of machine learning, geometry processing and industrial simulation. You will have the opportunity to explore