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Energy Institute aims to help to build a globally sustainable energy system, by bringing to bear multiple disciplinary perspectives to observe, analyse, model and interpret energy use and energy systems
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optical modelling and/or Monte Carlo simulations of light transport in tissue. Experience analysing complex physiological or biomedical datasets. Evidence of publication in peer-reviewed journals and
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author scientific publication/s in an international, peer reviewed journal/s. You will also possess research experience in enteric nervous system models/enteric nervous system stem cell biology
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, interpretability and cognitive modelling of language models. The group is part of the UCL ELLIS Unit and has links to the UCL AI Centre and UCL Computer Science. About the role The postholder will carry out original
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science LCN and UCL Physics and Astronomy are at the forefront of research into complexity in materials science, particularly in the area of emergent properties in strongly correlated systems. Particularly
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role The successful candidate will develop and evaluate AI-based spatio-temporal forecasting models for electricity demand at motorway EV charging hubs, integrating transport, charging, weather, and
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analysis of large, complex healthcare datasets alongside experience of writing papers for peer-reviewed journals. It is essential that candidates have a strong working knowledge of research methods in
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Fellow to join an exciting interdisciplinary research programme. This is a unique opportunity to investigate mechanisms of cancer dormancy and develop predictive models of late recurrence in oestrogen
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administrative health datasets. You will bring expertise in data linkage approaches, management of complex datasets, and handling missing data, as well as a clear understanding of data privacy and security
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Engineering or related topic. Working experience and knowledge of nonlinear numerical modelling of structure and infrastructure exposed to multi-hazards conditions Working experience of advanced programming