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The Oxford Agile Initiative has awarded funding for an exciting 12-month research project exploring the macroeconomic and distributional impacts of the UK’s Seventh Carbon Budget. Developed in close
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atmospheric plasmas are inaccurate and do not produce the correct compositions and energy distributions of expanding flows. The proposed research aims to make a step change in domestic and international
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. Armed with this information, the post holder will use cutting-edge paleoclimatic modelling that incorporates nutrient cycling and carbon chemistry (HadOCC) to infer the distribution of potential feeding
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distribution of immune cells using analyses such as immunohistochemistry or immunofluorescence. You will have expertise in developing multi-parameter (>4 colour) immunofluorescence approaches, experience in
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
of tropical forests in the Amazon and on how these relate to the distribution of vector-borne diseases in the Amazon forest. The post holder will carry out their research, advised by the PI and Dr. Milton
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for candidates interested in acquiring experience of research in a world-class academic unit (see http://www.wrh.ox.ac.uk). The post supports a Phase III programme of work aimed at testing and validating
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Colorectal Cancer - Stratification of Therapies through Adaptive Responses (CRC-STARS) programme, developing and applying cutting-edge mathematical methods to spatial transcriptomics imaging data in order to
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/workforce-and-learning-research-group ). The Compound Pressures project is a realist review funded by the NIHR HSDR programme. The project aims to understand how multiple and intersecting pressures, such as
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of agentic behaviour and publishing high-impact research. Candidates should possess a PhD (or be near completion) in PhD in Computer Science, AI, Security, or a related field. You will have a Strong background
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Oxford’s Department of Orthopaedics (NDORMS) as well as collaborators in Bristol and Cardiff. You should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely