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evaluation, policy advocacy, or better understanding the contexts and causes of such abuse. The student will use advanced data science and applied statistics to enable combined analysis of different modes
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will
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define observable events based on expert knowledge and available evidence. Development of a post-race analysis structure, process and data ‘toolkit’ that can build on historical understanding of race
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conducting the qualitative interviews with patients and analysing the interview data. You will be presenting that data to the study team at regular meetings and contributing to the intervention and app
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for household who stay indoors, and to prepare for emergency responses. Possible quantitative methodologies include concurrent time-series analysis of outdoor and indoor environment data, prediction model
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using a range of geoelectrical techniques, GHG soil flux measurements and associated soil-based analyses, as well as analysis of ecosystem GHG fluxes and remotely sensed data. We will support the PhD
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approaches, including principal component analysis and machine learning, to handle multivariate datasets. Prior experience in data science is not essential; the successful candidate will be supported through
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-funded Swansea University Research Excellence Scholarship (SURES) for a full-time doctoral student commencing in October 2025. Investigating functional genomic and multi-omic data analysis and integration
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been collected in Malawi under a joint agreement with the Government of Malawi, with field phenotype data, genomic sequencing data obtained in the UK, and micro-CT scanning data being also collected
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population-based studies. Using data from 11 million vessel segments from over 200,000 CFPs, we have shown that retinal vasculometry can predict cardiovascular disease as well as established risk scores and