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organisation across species. This will be a key pillar of a larger UKRI-funded research programme developing anatomy-driven artificial intelligence methods for fundamental and translational neuroscience. Working
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the impact of artificial intelligence on global societal outcomes through high‑quality, policy‑relevant research grounded in the social and computational sciences. The Initiative is internationally recognised
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are looking for candidates to have the following skills and experience: Essential criteria PhD in Natural Language Processing (NLP), Artificial Intelligence or a very closely related discipline. Strong research
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scientists and public policy researchers to inform and build pandemic risk policy and practice in the UK. The role-holder will contribute to a research project examining the impact of artificial intelligence
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innovative membrane module design, water purification and/or artificial intelligence-driven approaches for urban water management. However, the research programme can, in part, be tailored to prior expertise
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based in the Department of Informatics within the Faculty of Natural, Mathematical & Engineering Sciences (NMES), an internationally recognised centre for research in robotics, artificial intelligence
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Postdoctoral Research Associate who will primarily contribute to the project. The successful applicant will be ideally educated to PhD level in Computer Science, Artificial Intelligence, Software Engineering
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design selected technologies contributing to the research related to bioengineering, advanced materials or bioreactors for tissue engineering. Evaluate the possibility of applying artificial intelligence
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This postdoctoral position contributes to a UKRI-funded programme developing anatomy-driven artificial intelligence for translational neuroscience, with a focus on understanding how cognitive
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undertake high-impact research at the interface of materials science, advanced imaging, and artificial intelligence, with a particular focus on developing automated approaches for analysing multi-modal