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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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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
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. The successful applicant will work with experts in diabetes, data science, and genetics, and use detailed existing studies of hundreds of thousands of people with diabetes, to understand what is causing apparent