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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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/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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into the ethical governance of Large Language Models (LLMs), as part of the prestigious Divirsibus Vis Plurima Solvo project. The position is full-time and fixed term for 41 months or to the funding end date of 30
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take responsibility for the planning, execution and analysis of high-quality research, ensuring the validity and reliability of data at all times and will maintain ongoing scientific discussion with
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rules which enable effective learning in large and deep networks and is consistent with biological data on learning in the cortex. In particular, the research will focus on evaluating and extending a