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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
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We seek to appoint a Research Associate (postdoctoral scientist) to join the prolific climate/weather/environmental and impact science community at the University of Oxford, that is interested in playing a crucial part in the outcomes and deliverables of the multi-institutional NERC-funded...
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to understand and predict how technologies evolve — from artificial intelligence to net-zero innovations in energy, transport, and carbon capture. By building a global database on technological progress, we seek
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including conditional diffusion and flow matching models for synthesising Magnetic Resonance Imaging (MRI) and predictive analysis for Novartis Oxford collaboration for AI in medicine. The collaboration
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metrology, contributing to a UK-wide effort to improve the accuracy of PV energy yield prediction. All applications must be made online using the Oxford University E-Recruitment system, no later than 12 noon
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function. As such, we seek to uncover the fundamental mechanisms of healthy and accelerated brain aging by coupling mechanics and neurobiology to create multiphysics-informed predictive models of brain
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. This project will require a strong molecular biology background, knowledge in AI-based protein structural prediction, in vitro expression systems and cryo-EM, experience in analyzing large datasets, and solid
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interactions. We’re Looking for Someone With: Strong expertise in protein structure prediction, molecular modelling, and docking. Proficiency in LINUX, bash scripting, and high-performance computing environments
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Atmospheric Dynamics group in AOPP which looks at the role of dynamical processes globally in weather and climate variability, predictability and change. The role will involve detailed analysis and dynamical
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innovation in global health. You will work as part of the SchistoTrack team, a prospective cohort study that investigates predictive factors for schistosomiasis-associated morbidities across rural Ugandan