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environmental NCD risk score using statistical and ML methods. Prospective phase: Use wearable environmental and physiological sensors to track exposures and health parameters in healthy volunteers, translating
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for environmental epidemiology (Epi, survival, sf, gstat, mgcv) and causal inference (dagitty, MatchIt), as well as contributing to reproducible, scalable data pipelines. Machine learning integration: Exploring ML
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, and eager to apply computational skills to cutting-edge biological questions. In this project, you will develop a tool to infer karyotypes from individual cells based on their transcriptome, and use
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. This will involve investigating techniques for model compression and efficient inference to enable on-board condition monitoring directly at the wind turbine, reducing data transmission requirements, central
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research with numerical modelling. The group also has a strong track record in data-driven modelling of unsteady flows. This project explores how paired floating vertical-axis wind turbines can enable wind