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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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across historical, social science, and data science methods. While this studentship is grounded in historical and archival practice, candidates who wish to engage with quantitative, digital, or comparative
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scanning and measurement, garment fit evaluation, cloth simulation, digital twins, wearable sensing, smart textiles, and AI methods for clothing and body data analysis, to name a few. Applicants from
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be an integral member of the wearables group based at Oxford, led by Aiden Doherty. Our research team has access to world-leading population health data sets with objective wearable sensor measurements
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This PhD asks a different question: instead of demanding more data, can we build language models that learn smarter from less? You will design AI architectures that adapt to the structure of a
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tools and available data to analyse the cost-effectiveness of available interventions to reduce infections from a societal perspective. The student will have the freedom to shape the methodological
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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