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deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with experience in: Deep learning Medical imaging computing
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for more information. About you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria 1. PhD degree in Engineering, Computer
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that underpin virus infection, replication, transmission, zoonosis and pathogenesis. Projects employ a multi-disciplinary blend of experimental approaches that draws upon molecular-genetic, cultured cell
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. Advanced microscopy, cutting-edge biophysical techniques and genetically engineered mouse models will be used. The labs are well-established and located in the vibrant and supportive environment
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diabetes; genetics; infection and immunology; imaging and biomedical engineering; transplantation immunology; pharmaceutical science; physiology and women's health. We also have thriving research programmes
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Putts; and inflammatory genetics. The successful candidate will support other researchers and MSc/PhD students. They will develop novel scientific questions and lead on the analysis of specific aspects
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” of the brain is genetically determined, it is also influenced by environmental experience. We are still far from a complete understanding of how these processes work. CDN is one of four departments in the School
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” of the brain is genetically determined, it is also influenced by environmental experience. We are still far from a complete understanding of how these processes work. CDN is one of four departments in the School
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of Research Excellence and contribute to a growing cardio-immunology research programme. Inflammation is increasingly recognised as a key modifiable driver of adverse outcomes across a range of cardiovascular
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. About the role You will work closely with Prof. Hanna Kienzler, Prof. Stephani Hatch, and Dr Rebecca Rhead as part of the Centre’s ‘Marginalised Communities’ programme. This involves partnering with Black