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11th May 2026 Languages English English English The Department of Computer Science has a vacancy for a PhD Candidate in Hardware-Aware Deep Learning Model Optimization Apply for this job See
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to work on cutting-edge research at the intersection of deep learning and computer systems. The successful candidate will join an international and collaborative research environment and contribute
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, with a particular focus on identifying and characterizing rare endosomal escape events. The tasks include developing, training, and validating deep learning–based models for event detection and vesicle
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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employment. Starting date: 09.04.2026 Job description:PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 987 - 2026/WP 1 Work location: Hamburg Application deadline
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 2026
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computing, and synchrotron-radiation experiments. The goal is to explore physics-informed self-supervised learning approaches (e.g., deep image priors, GANs) and iterative methods, combined with multi-scale
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Doctoral Researchers (PhD students) to work on deep learning methodologies for machine and robot perception. These positions are funded by the Horizon Europe project OPERA (Open Perception, Learning, and
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. The tasks include developing, training, and validating deep learning–based models for event detection and vesicle tracking, and integrating these models into automated analysis and imaging workflows. The work