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Competitive athletes of various standards are continually striving to enhance exercise performance outcomes. A common approach to enhance exercise performance and post-exercise recovery is the
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machine-learning surrogate models capable of delivering near-DFT (density functional theory) accuracy in just a few CPU seconds per structure. This approach will enable the high-throughput screening of tens
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entrants without historical data for their land use technology differently compared to those with established practices? This interdisciplinary project offers the chance to contribute to multiple fields
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detected. As a result, UKHSA’s Real-time Syndromic Surveillance Team are working with N/LAB at the Nottingham University Business School to explore whether we can use anonymised sales of pharmacy over
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Location: South Kensington campus About the role: Applications are invited for a fully funded fixed-term position at the Research Associate (PostDoc) level in de-risking cirrus modification. Cirrus
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project funded by the Leverhulme Trust. Description below: The density of information content in screen media – i.e., the light, volume and pitch changes from which we extract meaning – has increased
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maintenance. However, current technologies are relatively slow and not capable enough to provide quick performance, diagnostic and prognostic predictions for real time applications. With the rapid development
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Closing Date Sunday 17 August 2025 Reference PHD002-25 A funded PhD position is available on a project funded by the Leverhulme Trust. Description below: The density of information content in screen media
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A funded PhD position is available on a project funded by the Leverhulme Trust. Description below: The density of information content in screen media – i.e., the light, volume and pitch changes from
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Evaluating the use of longitudinal over-the-counter healthcare sales data as a source of syndromic surveillance for community-based infections UK Health Security Agency and Nottingham University