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benefit from the extensive and broad expertise in AI and biomedical computing at the School of Biomedical Engineering & Imaging Sciences. The work will be done in close collaboration with a
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, biomedical engineering, medical imaging, or related field Experience in deep learning with practical implementation Strong Python skills and relevant frameworks Experience with large clinical imaging datasets
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About us The UCL Department of Medical Physics and Biomedical Engineering Department produces internationally leading research and integrated hands-on education in the heart of London, with close
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biomedical computing at the School of Biomedical Engineering & Imaging Sciences. The work will be done in close collaboration with a multidisciplinary team at KCL, UCL and with clinicians at Great Ormond
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(Mechanical Engineering at UCL) will also collaborate, he specialises in imaging of additive manufacturing and will support the project by assisting with the in-process monitoring. We expect that the PhD
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for real-time human data processing in interactive settings. Technical expertise in areas such as electrophysiological recording, VR paradigm design, closed-loop algorithm development, or clinical
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equipment and technology used to deliver minimally invasive endoscopic techniques to diagnose and treat lung cancer, and other cancers and diseases of the airway. We routinely perform advanced diagnostic
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biomedical computing at the School of Biomedical Engineering & Imaging Sciences. The work will be done in close collaboration with a multidisciplinary team at KCL, UCL and clinicians at Great Ormond Street
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Max Planck Institute for Human Cognitive and Brain Sciences • | Leipzig, Sachsen | Germany | 1 day ago
of Dresden, Faculty of Psychology (TUD) Institute of Cognitive Neuroscience, University College London (UCL), UK Teaching language English Languages Courses are held in English (100%). Full-time / part-time
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imaging, spatial data analysis, and machine learning. One arm of the project will seek to engineer diverse quantitative features (e.g., adapting concepts and metrics from network science [5] to characterise