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for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological
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ballot may leak the voter’s preferences if published publicly in plaintext, as the ranking order can reveal detailed information about the voter’s identity. Therefore encryption is necessary to hide
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will
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skills. Main duties will include: conduct tissue-mechanical and imaging experiments using early avian embryos; acquire and process data; prepare reagents and samples; optimise protocols; program and debug
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Fully funded positions available to start in 2025 or early 2026 Projects span plant genetics, climate-resilient breeding, AI and data science, soil health, sustainable agriculture, circular economy
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. This MSc by Research project aims to support these goals by experimentally evaluating advanced adsorption and ion exchange-based strategies for PFAS removal and contributing critical data to inform Anglian
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analysis methods. You will gain expertise in integrating experimental total scattering and high-resolution imaging data with artificial intelligence and atomistic simulation tools to overcome current
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(synoptic scale) forecasting (< 5 days) to mid-century scenario-based climate predictions (time horizon 2030-2050). The project will explore the merging of data sources to estimate future energy requirements
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sits at the cutting edge of materials science and solid mechanics, using real-world microstructure data to simulate thermo-mechanical behaviour at the grain scale. Your work will form a key part of a
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discounts, travel schemes and many more. To find out more about what we can offer you, follow the link to our benefits website Further information is available in the role profile. To apply for this vacancy