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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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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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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
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sits at the intersection of AI Safety and Data-Centric AI. We aim to make large-scale ML more reliable, transparent, and aligned with human values. We are specifically interested in: Data-centric AI
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venues and workshops) together with the ability to design, run, and make sense of large-scale experiments on foundation models. Informal enquiries may be addressed to Fazl Barez at [email protected]
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Supervisor: Dr Tim Halim Course start date: 1st October 2027 Project details For further information about the research group, please visit our website at https://www.cruk.cam.ac.uk/research-groups
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the landscape of novel target expression, quantify its heterogeneity, and the contribution of tissue architecture as a determinant of expression profiles. This project will involve large scale data processing and
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filters and observers, for real-time applications. Proficiency in MATLAB and Simulink for modelling, simulation, and validation using experimental or real-world data. Strong analytical, independent research