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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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offer the greatest opportunity for energy independence and deep decarbonisation (i.e. process heat and hydrogen) to meet net zero targets. Our programme of research, collaboration, and skills development
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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
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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
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) and mechanistic assays. • Proficiency in imaging and data analysis tools (e.g., CellProfiler, ImageJ). Knowledge & Experience (Desirable) • Knowledge of cancer biology and therapeutic resistance