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In this role, you will be responsible of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with
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Barcelona is seeking a highly motivated Computer Engineer to join our team and contribute to the development of a cutting-edge federated analysis infrastructure for the European repository of cancer imaging
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | about 2 months 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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of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In
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Vacancies Postdoc position on reinforcement learning on real-time image/video processing for medical robot Key takeaways In this role, you will be responsible of developing cutting-edge deep
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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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Post-Doctoral Position - Medical Image Processing - (240000Y9) Post-Doctoral Fellow Position in Medical Image Processing (Deep Learning for Trauma CT) The Trauma Radiology AI Lab (TRAIL) in
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signal/image processing, computer science, applied mathematics, or a related field, obtained less than 3 years before the date of hire. Essential skills, knowledge and experience: Excellent command
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hospital environment. Responsibilities and qualifications The principle responsibility of the postdoc is to implement an improved processing pipeline for SURE and ULM imaging, which can be used in both a
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researcher will carry out research tasks in IMAG, within the ERC NEMESIS project team, led in IMAG by two principal investigators, Jérôme Droniou (CNRS) and Daniele Di Pietro (University of Montpellier