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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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from animal hosts (are ‘zoonotic’), yet it is hard to predict which pathogen species are likely to cross into human populations and why. This project will combine AI Large Language Models, extracting
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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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developing the first observation-driven, AI-ready global river data infrastructure. Reporting to Professor Louise Slater, you will lead research combining large-scale Earth observation data, global hydrography
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chemically active drop. These microscopic drops use chemical energy from an ambient fuel to swim and explore their surroundings. Their appeal lies in their ability to be manufactured in large numbers via
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information-theoretic security even against quantum-capable adversaries, making it one of the most mature and commercially relevant applications of quantum communication. However, current QKD links are limited
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domains where data cannot be directly shared, such as healthcare and finance, the ability to selectively remove the influence of specific data from trained models becomes critical. Yet, despite the EU’s
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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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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