656 algorithm-development-"Multiple"-"Prof"-"Prof"-"Simons-Foundation"-"U.S" Fellowship positions in United Kingdom
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organisation, and with Homerton College, Cambridge, as well as with multiple external partners. We are seeking a talented research associate (Grade 7) or assistant research professor (Grade 9) with strong skills
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will be required to take a leading role across multiple projects, with support from other members of the team. Project topics are agreed with the funders in accordance with their priorities. KIS is a KHP
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foundational understanding of children's agency in digital environments increasingly dominated by AI systems. The UCL team is specifically focusing on the educational technology angle, developing agency
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will be required to take a leading role across multiple projects, with support from other members of the team. Project topics are agreed with the funders in accordance with their priorities. KIS is a KHP
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. In this role, you will be part of the research team, working to develop and evaluate privacy-preserved Generative AI algorithms for generating synthetic Personal Identity Information (PII). This aims
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research in fundamental & applied AI that is directly relevant to policy. to develop and deliver expert-led training on AI to civil servants across government. Fellows will spend 50% of their time on each
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, the role-holder will be part of the Centre’s central ECR cohort, with support from a cohort director / Centre deputy director and access to a Centre-wide ECR development programme—for example, co-writing
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, the role-holder will be part of the Centre’s central ECR cohort, with support from a cohort director / Centre deputy director and access to a Centre-wide ECR development programme—for example, co-writing
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demonstrate evidence of continuing professional development alongside excellent organisational skills with the ability to work flexibly and autonomously and contribute to / manage multiple projects at various
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partners. Main Duties Improve, develop, implement, and apply advanced computational tools and workflows to process, analyse, and interpret large-scale LCMS-based metabolomics datasets across multiple species