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and Edinburgh Universities, and led by UCL. The SOFAIR Lab will pursue fundamentally new AI architectures, moving beyond incremental refinement of existing models. By advancing open, distributed, and
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About us UCL is one of the world’s leading universities, renowned for disruptive thinking and research addressing major global challenges. Research, Innovation and Global Engagement provides
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(UCL). The successful candidate will join the internationally renowned Biomedical Optics Research Laboratory (BORL) and work within the research programme led by Professor Ilias Tachtsidis, who leads
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programme: Staff Support Service Discounted medical insurance Visit https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefits to find out more. Our commitment to Equality, Diversity and Inclusion As London’s
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for architecting, building and scaling UCL’s enterprise data platform. This includes modern data engineering, cloud data lakehouse development, distributed compute, data modelling, metadata/lineage services, self
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differentiation in UCL education and research. IT Service and Operations manages the infrastructure and data platforms which underpin UCL's central IT services, including data centres, cloud compute and storage
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About the Role We are looking for a Senior Computational Biologist to join the ERC-Synergy funded MUTAHOME project, a multi-institutional collaboration between Queen Mary University of London and
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of the interactions between building stocks, heat and cooling demand, and electricity distribution networks. They will develop new computational approaches, contribute to the expansion of existing modelling tools, and
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multimodality gastrointestinal, oncological, musculoskeletal and neurological imaging, as well as medical physics, statistical methodology, health economics, modelling, and health behaviour. The academic
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, interpretability and cognitive modelling of language models. The group is part of the UCL ELLIS Unit and has links to the UCL AI Centre and UCL Computer Science. About the role The postholder will carry out original