141 web-programmer-developer "https:" "https:" "https:" "https:" "https:" "https:" "University of Kent" Postdoctoral positions at University of Oxford
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literacy and managing online behaviours over the past two decades. We are looking for an individual who is interested in carrying out the project’s research programme under the supervision of Dr Ekaterina
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We are seeking a talented and motivated researcher to join the Mead Group to contribute to a major research programme focused on understanding and preventing disease progression in
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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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the EEE value chain. The PDRA will be responsible for working with industry partners to develop and evaluate product and system-level design solutions to enable effective reuse, repair and remanufacture
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is 12.00 midday on 10 April 2026. Interviews will be held as soon as possible thereafter. At the Dunn School we are committed to supporting the professional and career development of our postdocs and
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group at meetings. • You will also have a strong publication record in a related field, and possess sufficient specialist knowledge in the discipline to work within established research programmes
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, machine learning, and/or computational biology to be able to work within established research programmes. They will have excellent communication skills, including the ability to write for publication
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absorption analyses of photocatalyst function, including operando photoinduced absorption studies, and their correlation of materials structure, spectroelectrochemical analyses and hydrogen evolution
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shares and inheritance statistics in developing and developed countries. This is a fixed term role which will end by the end of February 2027. About you You will hold, or be close to completion of, a PhD
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funding. You will be responsible for the development of novel acquisition, reconstruction, image analysis and/or modelling methods for cerebrovascular magnetic resonance imaging (MRI) to improve