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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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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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high fidelity models of ice crystal icing accretion and shedding, verifying tools using the wealth of unique experimental validation data generated by researchers at the Oxford Thermofluids Institute
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knowledge and tools for non-equilibrium flows for hypersonic vehicles. The research will provide unique and high-quality experimental data for expanding high temperature flows. Alongside this, the proposal
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O’Brien’s research groups at the Department of Engineering Science (Central Oxford). The post is fixed term for two years and is funded by the EPSRC. The development of large-scale quantum computers will
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on funding) About the project: This project evaluates the effectiveness of anticipatory cash transfers in response to climate disasters, using large-scale RCTs in Bangladesh and other countries. It
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and demonstrated experience in cognitive neuroscience research. Experience with fMRI and behavioural data collection and analysis, with setting up and managing large databases, and knowledge
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/scripting (e.g., in Python, and/or R, and/or Matlab, and/or Bash script & NCO & CDO, etc.) and have demonstrable expertise in the analysis of big data, while the experience with interpretation of climate
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& NCO & CDO, etc.), and have demonstrable expertise in the analysis of big data, and the interpretation of climate/weather observations/reanalyses and model simulations. Additionally, experience with
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. The project will define new near miss and severe morbidity definitions allowing us to identify electronically when significant events happen. We will then develop a large multi-centre maternity routine dataset