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Field
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statistical approaches to characterise disease trajectories, develop risk prediction models, and identify factors associated with differential treatment outcomes. The findings will improve understanding of long
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significant reductions in manufacturing-related CO₂ emissions. The ambition is to develop innovative technological solutions that reduce energy losses and minimise environmental impact throughout the product
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Announcing 10 PhD Studentships within the Royal Holloway Social Purpose Centre for Doctoral Training
for the Everyday). We have an excellent Researcher Development programme, and wider institutional postgraduate training. We are committed to supporting a strong and growing PGR community, including PGR-Led
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on clinical complications, and use machine learning to develop and validate predictive models to identify high-risk patients. The research aims to individualise inpatient care, reduce hospital-acquired
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across development and aging. Using cutting-edge multi-omic and single-cell datasets generated by our team and international collaborators, this bioinformatics project will investigate how the X and Y
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disorder with debilitating effects on a patient's quality of life. Because current medications do not work well, we need to study its molecular cause to develop new therapeutics. Recent research identified a
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idea in more detail using novel genetic epidemiological techniques in large global biobanks. The student will train in 3 world leading centres and will gain skills in immunology, statistics and genetic
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with ordinary facial movement. While many methods have been developed to recognise and localise micro-expressions, these results remain difficult to understand. The proposed models will be designed
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VRGeoscience’s commercial platform VRGS. Objectives The objectives include: Build a benchmark dataset capturing how multiple experts interpret the same data, including their confidence in each judgement. Develop
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to build a strong academic or industry career. Objectives This project develops and evaluates modular, adaptive LLM architectures that allocate capacity according to linguistic complexity rather than corpus