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. The complexity and inconsistency of the data make it difficult to identify the true causes of poor outcomes. An emerging field in health data science is Causal Artificial Intelligence (AI). In traditional analysis
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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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. 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
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, observational data showed that protein intakes in ALL survivors do not differ from the general population, suggesting that the skeletal muscle may have an inability to respond normally to dietary stimuli, known
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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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interests, whether you prefer system-level analysis, control design, simulation-based investigation, or a combination of these. The specific research direction will be developed in collaboration with
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The continuous release of Earth Observation (satellite) data and the emergence of Machine Learning methods open up new possibilities for understanding forests. These large datasets provide
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nanofabrication, electromagnetic and micromagnetic computational modelling, theoretical and experimental analysis of electromagnetic properties of metamaterials, as well as the fundamentals of microwave
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