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relevant to TCR prediction, including machine learning, molecular docking, and related modelling strategies. Experience in systematic literature review and the integration of computational findings with
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processes, transport, and stress evolution interact at material interfaces, using multiphysics modelling and experiments to predict performance and durability. Who we are looking forThe following requirements
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Biology, Department of Life Sciences, to develop intelligent systems that integrate metabolic modeling, omics analysis, and automated literature mining. About us The Department of Life Sciences conducts
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microstructural descriptors that are physically meaningful and predictive. Probabilistic surrogate modelling and digital twin construction. The extracted microstructural descriptors will be used to learn a
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application! We are seeking a highly motivated PhD student to join a research project at the forefront of battery diagnostics and modelling, that will help shape the future of battery technology by developing
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processes, transport, and stress evolution interact at material interfaces, using multiphysics modelling and experiments to predict performance and durability. Who we are looking for The following
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physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead
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. Combining satellite, airborne and ground-based measurements with modelling and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. About the research
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understand the epidemiology of IBD, including how risk evolves over time and which factors influence IBD risk, such as medication use and comorbidities. The work will also involve prediction modeling and
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physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead