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, medical image reconstruction principles and experience with high-performance computer simulations in physics would be beneficial Experience in working with medical imaging data, especially with PET, and
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. What you should have: A 1st degree in physics or engineering. An interest in optics, some ability in computer programming A desire to learn new skills in complementary disciplines. You will work jointly
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20 Dec 2025 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Biomedical engineering Engineering » Computer engineering Researcher Profile
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of short-axis MR image sequences. Training You will be based at the Vision Computing Lab within the School of Computing Sciences, which specializes in deep learning for medical image analysis and neural
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to imaging with geophysical data. Our research focuses on mathematical methods for processing, imaging, and inversion of geophysical data, the physics of wave propagation, and the development of innovative
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phenomena such as velocity and homogeneity in hierarchically porous Si?based materials conduct multiscale imaging using MHz X?ray projection imaging, X?ray micro- and nano-computed tomography, and scanning
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 1 month ago
framework, medical image reconstruction principles and experience with high-performance computer simulations in physics would be beneficial # Experience in working with medical imaging data, especially with
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improvements in machine learning (ML) applications now allow researchers without extensive programming backgrounds to implement advanced image-processing techniques using accessible programming languages and
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 18 days ago
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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frameworks. This approach is timely, as improvements in machine learning (ML) applications now allow researchers without extensive programming backgrounds to implement advanced image-processing techniques