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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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dynamics and (at intermediate redshifts) strong gravitational lensing, thus preserving and extending the team’s lead in this field. Applicants should have a PhD (or close to completion) in (Astro) physics
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annum inclusive of Oxford University weighting Potential to under fill at grade 06RS: £34,982-£40,855 per annum inclusive of Oxford University weighting The Department of Computer Science seeks to employ
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inference attacks, to mitigate privacy leaks in MMFM. You will hold a PhD/DPhil (or be near completion) in a relevant discipline such as computer science, data science, statistics or mathematics; expertise in
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on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related discispline. You
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), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a PhD (or be near completion) in a field such as AI, computer science, signal
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will be required due to regulated activity involving children and ‘at risk’ adults. The successful candidate will hold, or have submitted a relevant PhD/DPhil (or equivalent) in a relevant discipline
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quality refereed journals and write reports for submission to research sponsors. You must hold a PhD (or be near completion) in a biomedical field of laboratory-based research (ideally immunology and/or
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have completed, or be close to completing, a PhD/DPhil in a relevant quantitative field such as computational social science, computer science, or cognitive science. They will have a demonstrable track