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About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat
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There exists an inherent variability in how lithium-ion batteries fail – an event commonly referred to as “thermal runway”. This uncertainty drives additional cost and complexity into the design and
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stacking error and removes the options for easy disassembly for repair, replace or recycle. In this project modification of the cell end cap design is to be investigated through FE analysis, prototype build
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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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processing using a state-or-the-art Gleeble HDS-V40 to test the materials behaviour to stress and recrystallisation kinetics at temperatures around 1400C. Alloy development to understand / design how
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designed specifically for the spatial and sequential structures inherent in XCT projections and reconstructed volumes. The goal is to achieve a 60-80% reduction in data size without compromising
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physics-based and data-driven methods to support the design and scale-up of these systems. This approach will reduce the need for costly experiments, improve scale-up predictions, and provide confidence
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, as even small delays can affect robot responsiveness and create safety risks. This issue is exacerbated by the fact that the modern Internet infrastructure was originally designed to provide best
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the THz labs at Warwick and by our collaborators at the Institute of Saint-Louis (ISL). By suggesting design modifications to the molecular structures, your work will improve the next generation of
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resolution. By combining quantum-mechanical modelling, tomographic reconstruction, and data-science methods, the project will reveal how skyrmions interact with defects, helping to design the next generation