93 modelling-complexity-geocomputation Postdoctoral positions at University of Oxford
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project focused on systematically exploring the impact of the exposome on complex disease risk, through the lens of multi-omics data (e.g., genomics, proteomics, metabolomics and biochemistry) from large
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responsibility for carrying out research exploring connections between probability and number theory, as part of the EPSRC grant “Extreme Values of Complex Systems: Random matrices and L-functions”. Candidates
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will develop novel tools which will allow efficient flow modelling tools for other researchers to explore higher fidelity thermochemistry modelling. The main responsibilities of the post will be
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for this post. The successful candidate will be required to develop a personal research programme in theoretical cosmology (which may include numerical modelling and/or data analysis), interacting with faculty
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Professor Chris Russell. This is an exciting opportunity for you to work at the cutting edge of AI, contributing to a major shift in how we understand and apply foundation models. The position is full-time
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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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models to study direct interaction between macrophages and other tissue resident cells. Additionally, you will test hypotheses and analyse scientific data from a variety of sources, reviewing and refining
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security screening before you can commence in the role. About you The successful applicant will be able to present information on research progress and outcomes, communicate complex information, orally, in
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samples and disease models. Working closely with a dynamic and multidisciplinary team of clinicians and scientists, you will help generate and interpret high-resolution datasets that reveal new insights
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, Oxford, Leeds, Reading, and Birmingham) and international (Utrecht University, ETH Zurich, Université Catholique de Louvain, etc.) scientists to use new modelling resources and methods to elucidate drivers