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knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux systems and High Performance Computing Good oral and written presentation skills in Norwegian
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Application deadline: October 30, 2026 VIB-KU Leuven Center for Neuroscience is recruiting enthusiastic, creative, ambitious, and highly motivated PhD candidates to join us starting between May
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Area of research: PHD Thesis Job description:PhD Candidate (f/m/x) in Data Science for Biomedical Imaging
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of novel volumetric representations and computational imaging methods for paired visible-light and X-ray images, as well as extending computer vision techniques from the visible-light RGB photography domain
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hiring test may be part of the procedure. Read here whether that applies to you. If you join us, we ask you for a VOG (Certificate of Good Conduct). Internal candidates will be given priority over
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opportunity to perform cutting-edge research that combines hardware development, signal processing, AI-driven image analysis, and clinical translation. Where to apply Website https://www.academictransfer.com/en
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the development of novel volumetric representations and computational imaging methods for paired visible-light and X-ray images, as well as extending computer vision techniques from the visible-light RGB
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application process here. About the position Are you motivated to develop next-generation research infrastructure at the intersection of robotics, mechatronics, sensor systems, and life science technology? We
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higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming
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-inspired computing paradigm with the potential to drastically reduce energy consumption while enabling faster inference than conventional digital architectures. A major challenge, however, is the development