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, combustion, and process optimisation. The project is focussed on the development of novel interface capturing Computational Fluid Dynamics methods for simulating boiling in Nuclear Thermal Hydraulics
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duration: October 2025, for 3.5 years. Candidates must possess or expect to obtain, a 2:1 or first-class degree in Engineering, Physics, Chemistry, Materials Science, or related physical sciences discipline
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of deep learning models, especially when new training experiences are corrupted. The framework will be validated in robotic control scenarios during EV battery assembly, under process variations such as
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, electrochemical characterisation, or related fields. A strong degree (first or high upper second) in Materials Science, Mechanical Engineering, Chemical Engineering, Physics, or a related discipline is preferred