648 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"UNIS" Fellowship positions in United Kingdom
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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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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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, 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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development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning algorithms might support the wider integration of, and uptake
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multiple formats. Characterisation will feature strongly in terms of structure / chemistry characterisation to underpin the electrochemistry developments. CLOSING DATE: Tuesday 15th July 2025 at 23:55 (BST
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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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We are seeking to appoint a Clinical Research Fellow who will have specific responsibility for research and for developing research objectives and proposals for the Digital Cancer Screening Research
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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