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project coordinator and research assistant position to support two large projects: 1) a Royal Society funded fellowship: Harnessing island-ocean connections to support ecosystems and people, and 2) a UKRI
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advanced statistical modelling, econometrics or machine learning with a particular focus on analysing large-scale behavioural data? Are you interested in developing new methodological approaches capable
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or machine learning with a particular focus on analysing large-scale behavioural data? Are you interested in developing new methodological approaches capable of extracting behavioural insight from increasingly
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requires complex and time-consuming workflows that limit their use in large patient populations and routine clinical practice. This project aims to develop new methods for personalising cardiovascular
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an experienced, motivated quantitative Research Training Fellow to lead data integrative analyses in large-scale epidemiological studies of cancer and apply innovative methods in epidemiology, statistics and data
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, the Research Fellow will execute defined research workstreams, contribute to analytical strategy, and generate reproducible and clinically meaningful insights from large-scale genomic and health data. Research
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at each institution as part of the five-year STFC Large Award "TRANSCEND". TRANSCEND is a collaboration between QUB/Lancaster/Oxford/Southampton that will use Vera Rubin/LSST transient data and the 4MOST
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at each institution as part of the five-year STFC Large Award "TRANSCEND". TRANSCEND is a collaboration between QUB/Lancaster/Oxford/Southampton that will use Vera Rubin/LSST transient data and the 4MOST
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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Invention Agency (ARIA), working at the intersection of Isabelle/HOL, the seL4 verified microkernel, information-flow security, and AI-assisted theorem proving. The project aims to develop a formally-verified