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machine learning tools and working on Linux High-Performance Computing platforms would be highly desirable. This is a highly collaborative role and you will work with scientists and clinicians from other
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development laboratories at Guy’s Campus, London Bridge. The group specialises in inventing custom fluorescence-lifetime and multiphoton technologies and coupling them with powerful computational pipelines
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at the Barts Cancer Institute (Queen Mary University of London). This role will involve analysing existing spatial-omics data sets and developing novel computational tools to understand the risk of developing
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spectroscopy Experience Proven experience in algorithm development to do physiological signal processing Experience in advanced computational techniques to assess and enhance signal quality Experience in machine
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"Mathematical Data Science" research group at the University of Vienna (led by Prof. Dr. Philipp Grohs) and the "Computational Partial Differential Equations" research group at TU Wien (led by Prof. Dr. Michael
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students and over 500 Single Honours undergraduate students. We offer a 3-year BSc degree programme in Biological Sciences and a 4-year MBiol, while also contributing to Natural Sciences. We also run a PGT
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at the Barts Cancer Institute (Queen Mary University of London). This role will involve analysing existing spatial-omics data sets and developing novel computational tools to understand the risk of developing
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Trust Career Development award funded research programme “Unlocking the RNA Helicase Code: From Structural Insights to Therapeutic Strategies” led by Dr. Jagtap. The overall project builds upon our novel
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programme as part of the collaborative NINEDTP. The Department’s research strategy is built around five themes: Communities and Social Justice; Health and Social Theory; Higher Education and Social
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of Biomedical Engineering and Imaging Sciences is a cutting-edge research and teaching School dedicated to development, translation and clinical application within medical imaging and computational modelling