656 algorithm-development-"Multiple"-"Prof"-"Prof"-"Simons-Foundation"-"U.S" Fellowship positions in United Kingdom
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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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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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multiple stakeholders, you will have excellent communication and interpersonal skills. We are particularly interested in receiving applications from candidates who have a desire to develop as a global mental
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quantitative data analysis of large secondary datasets and be based at the University of Bristol. There are opportunities to work on several other ongoing and upcoming research projects to help develop your
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at Cambridge. The Fellow will work under the supervision of the Principal Investigator and Director of LISA, with a view to developing LISA's research capacity in spatial analysis of housing inequality
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). This role is an exciting opportunity to take a role in the development, management and analysis of a new repository of Individual Participant Data (IPD) from ageing research trials. The work is an
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-led by Queen Mary University of London. PharosAI is set to revolutionise AI-powered cancer care, accelerating the development of breakthrough therapies, advancing clinical applications, and improving
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these interactions we are working to develop better diagnostic tests and future vaccines and novel therapeutics. Staphylococcus aureus is a major opportunistic pathogen and the leading bacterial cause of death in most
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the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience against different interfering systems. Develop, with