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physics, statistical methodology, health economics, modelling, and health behaviour. The academic department is based in the Rayne building University Road, with a secondary office in the Imaging Division
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& Responsibilities: Develop advanced deep learning methods for radiology or pathology medical imaging Integrate imaging data with EHR, clinical notes, or genomic data Conduct research on segmentation, classification
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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: The Computed Tomography division encompasses stationary CT equipment located in University Hospital, East Hospital, James Cancer Hospital and OSUWMC Ambulatory Imaging locations. Position Summary The CT
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detailed tasks accurately and with quality. The position will contribute to developing and/or applying artificial intelligence, machine learning, and image segmentation and/or data science-based methods in
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The MR Division encompasses MR equipment located in University Hospital, East Hospital, James Cancer Hospital, and OSUWMC Ambulatory Imaging locations. The MR Division offers MR imaging services
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variational autoencoders (VQ-VAE) or similar models Knowledge of MRI physics and Bloch equation simulations Experience with medical image segmentation or classification tasks Publications in machine learning
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for the division, under the direction of the Imaging Manager. Minimum Qualifications 2-Year College Degree. A.R.R.T. Radiography registered with current ODH licensure. Previous interventional radiology experience is
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About Us The post will be based at St Thomas’ Hospital in central London in the School of Biomedical Engineering & Imaging Sciences at King’s College London: https://www.kcl.ac.uk/bmeis . There is
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, this segmentation may rely on artificial intelligence (AI) tools trained to automatically identify and delineate the main organs from MR images. This step will make the generation of attenuation maps faster, more