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Field
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. The position concerns research in the above-mentioned projects. Requirements PhD degree in Medical Image Processing or a foreign degree equivalent to a PhD degree in Medical Image Processing. The degree needs
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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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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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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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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
radiologists involved in the projects. Training and research environment The Brain Imaging & Neuro Epidemiology group (BraINE) develops advanced methods for medical images analysis, combining computer vision
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conduct comprehensive and systematic literature and database searches related to self-supervised learning, medical imaging, and population-level phenotyping. About You You will have or be close to the
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The Center for Intelligent Optics (CIO) develops advanced optical technologies, including imaging, spectroscopy, and laser ablation methods. Our goal is to bridge these cutting-edge laser
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medical images and clinical text. The primary focus of this role is to develop foundation models that can be applied downstream to clinical triaging tasks—helping prioritise cases based on MRI imaging data
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 3 days ago
function using functional MRI or ultrasound imaging. The Max Planck Institute for Biological Cybernetics conducts research that elucidates neurological processes across various organizational scales via
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corresponding radiology reports during training to build comprehensive representations that capture the rich, complementary information contained in medical images and clinical text. The primary focus