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About the project: Developing a Theory of the Magnetic Fingerprint of Stress in Materials Supervisor: Dr Chris Patrick, University of Warwick In the development of sustainable materials and
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methods to accelerate the discovery of better catalysts that use less platinum and have improved long-term stability. By combining large-scale density functional theory with machine-learned interatomic
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optimisation – to enhance the ruggedness and efficiency of power converters for grid-scale and high-power-density applications. The student will be supervised by Dr. Jiaqi Yan and Prof. Layi Alatise based
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manufacturing, yet their performance is fundamentally limited by our inability to precisely control particle alignment and microstructure during fabrication. Existing methods—such as magnetic or electric field
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to run these algorithms, i.e., the AI data centers, are extremely power hungry, thus significantly increasing the burden on the electrical grid. More importantly, the unique AI data centres load patterns
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and next‑generation manufacturing, yet their performance is fundamentally limited by our inability to precisely control particle alignment and microstructure during fabrication. Existing methods—such as
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) at WMG, University of Warwick to work on: Environmental effects on the long-term mechanical performance of short-fibre-reinforced thermoplastics (ENFORM) Short-fibre reinforced thermoplastics (SFRPs
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frameworks to maximise compression ratios. We will use predictor models which estimate projections or slices, storing only differences between the prediction and original data. Because errors are small and
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) software using PyTorch, making the tools immediately accessible to the wider scientific community. The student will work across the University of Warwick (WMG) and the Harwell Science and Innovation Campus
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systems remain too complex for widespread commercial use. This project aims to overcome these barriers by developing a high‑resolution spatial light modulator based on high‑aspect‑ratio silicon pillars