163 phd-in-mathematical-modelling-population Postdoctoral positions at University of Oxford
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interpretation of atmospheric circulation in high-resolution reanalysis data, idealised model simulations and a state-of-the-art weather forecasting system. The post-holder will have the opportunity to teach
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Claudia Monaco’s research group at the Kennedy Institute of Rheumatology. In this role, you will apply single cell biology and cell signalling techniques combined with in vivo and in vitro models
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Economics Research Centre (HERC), Nuffield Department of Population Health, with the opportunity to also contribute to HERC’s varied programme of teaching. You will manage your own academic research and
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Oxford Population Health (Nuffield Department of Population Health) is home to world-renowned population health research groups and provides an excellent environment for multi-disciplinary research
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structural), ECG, and genetics, to model disease trajectories and improve risk prediction in cardiomyopathies. The successful applicant will work closely with the PI to deliver research projects, supervise
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should have a PhD (or close to completion) in Physics, Planetary Sciences or Earth Sciences. It will be an advantage to have experience in remote sensing, analysis of thermal data, thermal modelling
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especially suitable for someone with strong formal reasoning and data analysis skills who is considering progression to a PhD or further postdoctoral research in AI ethics, social choice theory
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly
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Haematology Unit. You will use state-of-the-art genetic tools and functional genomics to generate and characterize models of CH and ageing, including the role of the bone marrow microenvironment in
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
, epidemiology, and socio-environmental modelling. To be considered a successful candidate; A PhD degree in Ecology, Biodiversity analyses, Environmental Science, Remote Sensing, Epidemiology, Data Science, or a