36 phd-position-in-image-processing-"Naturalis" Postdoctoral positions at University of London
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microscopy analysis -e.g. FlowJo and image J and software for their quantitative analysis- would be essential. About the School/Department/Institute/Project The position is within the Centre for Biochemical
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potential applications in audio and music processing. Standard neural network training practices largely follow an open-loop paradigm, where the evolving state of the model typically does not influence
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on autofluorescence (AF) imaging and Raman spectroscopy for detection of metastatic lymph nodes during breast cancer surgery. Engaging with and reporting to Dr Alexey A. Koloydenko (Department of
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treatments. To achieve this, we will develop personalised cardiac models at scale, and update these models over time, using imaging and electrical data collected by collaborators at multiple centres. We
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About the Role We are recruiting for an 18-month postdoctoral position as a part of an MRC funded project. This position will focus on investigating bacterial and host factors influencing early
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to their own research interests. About You Candidates should have a PhD in a relevant discipline or will have obtained it by commencement of the position. Candidates should have some experience in multi
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-luminosity LHC. You will provide support to our PhD students and contribute to the broader activities of the group and the school. About You You will have completed or be about to complete a PhD or research
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physiologically relevant models will provide crucial platforms to mimic disease pathology, and better understand and treat tendinopathy. The project will generate tendon-chips using in-house commercially available
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into clinically meaningful insights. About You We are looking for a motivated researcher with a PhD (or near completion in 2025/26) in statistical genomics, genetic epidemiology, bioinformatics, or a related field
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal