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Field
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Additive Technologies) team at University of Warwick seeks a creative and motivated research fellow in the area of deep learning for (X-ray) image processing. The appointment is currently until 31 March 2027
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to produce cutting-edge research. Prospective applicants must: Hold a good honours degree in an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine
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of Biomedical Computing as part of the School of Biomedical Engineering & Imaging Sciences, King’s College London, a vibrant community of engineers designing and translating technology into the clinical
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methods, signal processing techniques, and comparison with alternative detection technologies. ii. Support design and development of NQR prototype, including system architecture, component selection, and
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technologist to join our PET imaging team at King’s College London (KCL) & Guy’s and St Thomas’ (GSTT) PET Centre. The Centre is part of both King's College London's School of Biomedical Engineering & Imaging
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an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine learning techniques and experience with coding in Python is beneficial (but not a strong requirement
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push the boundaries of neural scene representations in a medical imaging context. The successful candidate will work alongside a multidisciplinary team of deep learning researchers, computer vision
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both King's College London's School of Biomedical Engineering & Imaging Sciences (BMEIS) in the Faculty of Life Sciences and Medicine, and Guy's & St Thomas' National Health Service (NHS) Foundation
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collection activities. Supervision will be provided by academics from various disciplines specializing in biomechanics, image processing, and computer vision, alongside orthopaedic surgeons and academics.
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(or equivalent) in a biomedical science. Experience in neuroscience and/or immunology is desirable. Project key words Retinal imaging, data-analytics, computer vision, big data Funding The studentship, funded by