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Metabolism (OCDEM) on studies related to circadian rhythms in population health. This post is part of a large, interdisciplinary research programme, offering attractive opportunities to work across
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of Edinburgh and Glasgow, with expertise across computer science, wearable computing, AI and criminology. Our programme of research utilises both experimental and literature-based approaches to explore
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the Department of Engineering Science at the University of Oxford. The post is funded by the Oxford Martin Programme on Circular Battery Economies. It is fixed term up to December 2027. You will undertake
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for future research collaboration. To plan and manage own research activity, research resources in collaboration with others and contribute to the planning of research projects. To deliver training in research
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Banking. Your skills and attributes for success: Expertise in one or more of the following areas: Machine Learning, Reinforcement Learning, or LLM orchestration. Strong programming skills in Python and
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of nitrides, along with Raman spectroscopy measurements. This programme of research will be conducted in collaboration with researchers from the University of Bayreuth (Germany), the Commissariat à l’Énergie
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, computer science, statistics, or a related field together with strong programming skills in Python, R, or similar languages, and proficiency in high-performance computing. You will have experience in large-scale
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for medicine use before and during pregnancy. This postholder would work primarily on a recently funded programme of work to develop a novel approach to understanding and communicating the Safety of Medicines in
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. Proficiency in the use of statistical programming languages and analysis of large datasets and strong publication records would be essential. Previous experience in atmospheric dynamics and predictability is
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available from 1st September 2025 for one year in the first instance. The successful applicant will plan, conduct and analyse qualitative ‘think aloud’ interviews with those living with multiple long-term