18 bio-inspired-computing Postdoctoral positions at University of London in United Kingdom
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at the Blizard Institute to build and test custom made tendon-chips, using advanced bio fabrication techniques. You will join the Screen research group - a multidisciplinary team of scientists from engineering and
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at the forefront of contemporary trauma care. About You The post holder must be pursuing / completed a PhD degree or equivalent in Bio Medical Science related field. Pursuing / completed PhD degree or equivalent in
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and statistical modelling, statistical image analysis and computer vision, chemometrics, biophysics, bioengineering. Preference will be given to candidates with a demonstrated experience in applying
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computational methods (Efremova et al. Nature Protocols, 2020; Jain et al., Genome Biology). About You PhD in a biological or computational subject and background in working with genomics data. About the School
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motivated computational Postdoctoral Research Assistant to lead on an established and successful research line aimed at understanding the genetic events that drive cancer evolution. We have a long-lasting
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About the Role The purpose of this role is to provide qualitative and quantitative research support for a research and impact programme on food reformulation. This role sits within the Research and
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About the Role We are looking for a Postdoctoral Research Assistant to work with Dr Chema Martin on a Human Frontiers Science Program Research Grant project entitled “Evolutionary Biophysics
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About the Role This role will involve undertaking the evaluation of a digital social intervention in primary care in England. A summary of the programme grant is found here. The individual will be
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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
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals