82 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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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
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form, how it is generated and how it evolves. In particular we focus on the evolution and evolvability of vertebral counts, and we use various species of Lake Malawi cichlids as our model organism