136 postdoc-position-in-image-coding Postdoctoral research jobs at University of Oxford
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together to answer some of the most important questions about the causes, prevention and treatment of disease. We are seeking to appoint a Postdoctoral Researcher in Biomedical Image Analysis to join the
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We are seeking a full-time Postdoctoral Research Assistant in “Resilient Temperature- and Pressure-Sensitive Paints for Hyperspectral Imaging” to join Prof. Jin-Chong Tan’s group (Multifunctional
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quantification. Find out more about the research and group here. Your Role As a postdoc on this project, you will be part of a dynamic team working at the intersection of computational biology, molecular
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and temperature on overall mechanical response, and will have the opportunity to field high-speed imaging, digital image correlation, laser velocimetry, and related techniques to enhance the type and
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team, and independently, are essential. You will also provide guidance to less experienced members of the research group, including postdocs, research assistants, technicians, plus PhD and project
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of the research group, including postdocs, research assistants, technicians, plus PhD and project students. You must have: A relevant PhD/DPhil (or be close to completion), together with relevant experience in
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period of 12 months in the first instance, with potential to extend. The project involves advanced theory for new ultrafast imaging experiments and is funded by Engineering and Physical Sciences Research
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including conditional diffusion and flow matching models for synthesising Magnetic Resonance Imaging (MRI) and predictive analysis for Novartis Oxford collaboration for AI in medicine. The collaboration
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(computational and experimental postdocs, PhD students, research assistant) with access to cutting-edge experimental and computational facilities. The postholder will have the opportunity to regularly interact
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used in our work centre around optical imaging and spectroscopy and nanofabrication. The work also relies on theory and simulation, specifically focusing on numerical mean-field electrostatics