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/video segmentation, object tracking, reinforcement learning. Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation
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of the following areas: Image processing, partialdifferential equations, scientific computing, deep learning.•Solid programing skills. Knowledge of Python, Tensorflow/Pytorch, and Mathematica.•Fluent
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Treatment. The post-holder will work in the School of Biomedical Engineering & Imaging Sciences, King’s College London, with a team of investigators covering AI, computer vision, robotics, and medical imaging
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School of Biomedical Engineering & Imaging Sciences, King’s College London, with a team of investigators covering AI, computer vision, robotics, and medical imaging. You will join a dynamic and successful
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scienceYears of Research Experience1 - 4 Research FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria Profile: Doctorate in Computer Science or Image and Signal
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The Biomedical and Astronomical Signal Processing (BASP ) laboratory at Heriot-Watt University Edinburgh (HWU ), headed by Professor Yves Wiaux, is recruiting a postdoctoral researcher in computational imaging
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | 11 days ago
Post-doctoral Position In Medical Image Processing The Computer-Assisted Medical Interventions (CAMI) team at the TIMC laboratory (Grenoble, France) is seeking a highly motivated postdoctoral researcher
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-national ERC Synergy Programme, EndoTheranostics, aiming to revolutionise Colorectal Cancer Treatment. The post-holder will work in the School of Biomedical Engineering & Imaging Sciences, King’s College
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Astrophysics (MPO), Stellar and solar Physics, Signal and Image processing (Signal), Theory and Observation in Planetology (TOP) and Turbulence, Fluids and Plasma. It is the birthplace and remains a world leader
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intelligence-based automated interpretation of medical images, and new knowledge on the human visual system into the screening mammography reading process. Combining these new capabilities and new knowledge has