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on glacier behaviour, including extreme events. For this work you should be familiar with image (pre-)processing techniques to obtain high quality quantitative data from time lapse imagery. This may include
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of Python libraries, building on existing prototypes. Construction of a computer vision pipeline to aid knowledge graph construction. Implementation of a prototype interface, developed with the design team
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, conservation and heritage science. The postholder will be based in the Department of Applied Mathematics and Theoretical Physics within the Cambridge Image Analysis group, a research team working on mathematical
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to self-supervised learning, medical imaging, and population-level phenotyping. About You You will have or be close to the completion of a relevant PhD/DPhil (e.g. in Computer Science, Engineering, or
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completion of a relevant PhD/DPhil (e.g. in Computer Science, Engineering, or Medical Image Analysis) and possess sufficient specialist knowledge in medical imaging—particularly whole-body and abdominal MRI
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About Us The appointee will join the School of Biomedical Engineering & Imaging Sciences. About The Role The research associate will lead the development of cutting-edge multi-modal MRI foundation
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of Biomedical Engineering & Imaging Sciences. About The Role The research associate will lead the development of cutting-edge multi-modal MRI foundation models. These models will leverage both imaging data and
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, and recommend personalised treatment for musculoskeletal conditions. Key skills will include deep learning, convolutional neural networks and transformers, multimodal AI and fusion modeling. Imaging
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, and recommend personalised treatment for musculoskeletal conditions. Key skills will include deep learning, convolutional neural networks and transformers, multimodal AI and fusion modelling. Imaging
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departments: Cardiovascular Imaging, Cancer Imaging, Early Life Imaging, Imaging Chemistry & Biology, Biomedical Computing, Surgical & Interventional Engineering, Imaging Physics & Engineering and Digital Twins