17 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Exeter
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therapies emerge and precision medicine become increasingly achievable, understanding this heterogeneity is critical. This PhD will use large-scale routinely-collected healthcare data and cutting-edge
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by people with diabetes, who often experience poorer clinical outcomes due to unstable blood sugar levels (adverse glycaemia). This project harnesses cutting-edge electronic patient record data
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to simultaneously learn control policies and safety certificates—mathematical proofs that control decisions are safe. Data from system operation provides evidence that both the control and the proofs
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other environmental exposure are associated with cognitive decline in later life. The student will link environmental exposure data to longitudinal cognitive and health outcomes, identify vulnerable
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healthy despite being at high genetic risk of dementia? This project will use cutting-edge genetic and molecular data from large human biobanks to identify molecular factors that help protect against
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generate biologically interpretable models of zoonotic emergence and ultimately aid in developing new tools for human pathogen surveillance. This project offers training in bioinformatics, data science and
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predicts something important? This project will uncover the neural circuits that transform visual information into dopamine signals that teach the brain about motivation, action and decision-making
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and machine learning methodology to help deal with key challenges in developing such models in large-scale observational electronic healthcare record data. These models will be applied to important real
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for place with the projects across the MRC GW4 BioMed3 Doctoral Landscape Award studentships. There are a total of 18 studentships available across the partnership. This exciting PhD combines big data and
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knowledge graph from scientific papers and cognitive test questionnaire data, and second, to integrate the graph with transformer-based large language models and causal learning. This offers an explainable