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We are looking to appoint a postdoctoral researcher, to work with a group of UK Higher Education Institutions to deliver a programme of mental health research. The work is funded by the Medical
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computing and associated increases power demand and procurement costs. This role will support the creation of a network of experts, a team of champions and a portfolio of community projects to provide UKRI
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computational workflows on a high-performance cluster. You will test hypotheses using data from multiple sources, refining your approach as needed. The role also involves close collaboration with colleagues
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hepatitis and liver disease. This post is funded by the National Institute for Health and Care Research (NIHR) as part of a significant research programme that leverages large-scale healthcare datasets
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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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midwives, nurses, patient and public contributors, as well as NHS sites who are providing data for the programme. As a Medical Statistician you will contribute to a wide range of tasks across the project
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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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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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with a PhD in Engineering (or close to completion) may apply. You will be responsible for: Design of the first reconfigurable robotic matter in collaboration with world-leading universities and
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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