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physics-based insights with data-driven methods—such as physics-informed neural networks, surrogate models and Bayesian optimisation—to explain formation behaviour, identify early indicators of cell
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and conducting original research and through publication as appropriate to the disciplinary area. To deliver research related to the project “AI enabled Satellite Morphological Inference from
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postdoctoral researchers and has proven track-record in mentoring and supporting the development of early-career researchers. The profile and research interests of the group may be found at: https
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deadlines Person Specification First degree in area of specialism and normally, a higher degree relevant to research area or equivalent qualifications High level analytical capability Track record of high
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relevant to research area or equivalent qualifications High level analytical capability A good publication track record Ability to communicate complex information clearly Fluency in relevant models
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ReLiB project, on the recycling of lithium-ion and next generation batteries. The ReLiB consortium has successfully embedded Life Cycle thinking, highlighting the need for tracking environmental and
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experiments Experience in scientific computing relevant to an online environment Proven track record of research in experimental particle physics High level analytical capability Ability to communicate complex
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talks or posters at national or international conferences Desirable: Having understanding of patient experience in cardiac diseases High-level analytical capability, with a proven track record of problem
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strong track record in programming, preferably in Python. The candidate should have demonstrable expertise in computational modelling of solids, preferably using density functional theory methods. Previous