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satellites, with the potential for travel to test instrumentation in ideal locations. Additionally, the simulation work will focus on developing computational models to validate instrumentation and optimising
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used. AI methods for generating regulatory hypotheses between genes, hormones and physical properties will also be developed. Applicants must have/be close to obtaining a PhD or MPhil in Computational
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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apparatus equipped with thermocouples and thermal imaging to simulate realistic runaway events. Top-performing coatings will be validated in situ on live EV cells under controlled runaway conditions. Dr
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enhance system reliability and safety, aligning with the UK’s NetZero targets. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess
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. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess the structural performance of composite sleeves under operational conditions
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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modelling tools to understand and tailor the physical and chemical interactions at the interfaces within metascintillators. Cranfield University’s Centre for Materials is internationally recognised
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield