181 parallel-and-distributed-computing-phd Postdoctoral positions at University of Oxford in Uk
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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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expected that the successful candidate will contribute to the computational analysis of multi-omic data that will be generated during the project. The candidate must hold or be near completion to a PhD/DPhil
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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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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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. Concurrently, you will develop lower order analytical models and perform high fidelity computational simulations to corroborate experimental findings and propose other configurations to be subsequently
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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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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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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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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