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to information. We work on search engines, on recommender systems, and on conversational assistants. There is a heavy emphasis on data-driven methods, for understanding content, for analyzing and predicting user
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vivo (e.g., brain organoids) and in vivo (e.g., mice) experimental models. Our Group collaborates with colleagues based in two international consortia: CHARGE and ENIGMA. Our research takes place in both
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rupture, which is one cause of a stroke and thus the prediction of plaque rupture is very relevant. The steps in the development of surrogate models are building data-driven models from medical imaging
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of ecological data and sustainability issues - Materials Science: AI-driven discovery and design of new materials Applicants should have (i) a Ph.D. in Computer Science, Computer Engineering, Electrical
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Centre (STARC) The Energy Environment Water Research Centre (EEWRC) The Climate and Atmosphere Research Centre (CARE-C) The Science and Technology Driven Policy and Innovation Research Centre (STeDI-RC
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modelling and dynamic material planning and production and scheduling into an actionable decision-support toolkit; Embedding explainable AI to ensure planners and engineers understand, trust, and use
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Environment Water Research Centre (EEWRC) The Climate and Atmosphere Research Centre (CARE-C) The Science and Technology Driven Policy and Innovation Research Centre (STeDI-RC) Considerable cross-centre
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University of London was established in 1966 and is a leading multidisciplinary research-intensive technology university delivering economic, social and cultural benefits. For more information please visit
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, processing, and integration of country-specific data for economy-wide energy system decarbonization models, in the production of spatially granular and engineering driven representations of model results
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an approachable and highly experienced research team. You will explore cutting-edge topics in specification-driven development, large language models, and AI-assisted software engineering. Your job As a PhD