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, and deployability of deep learning models on resource-constrained edge platforms. The PhD candidate will collaborate closely with international project partners and contribute to advancing next
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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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endosomal escape events. 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
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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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, we need an imaging scheme that captures relevant features at different length scales and integrates them into a single reconstruction volume. This PhD project focuses on learning-based phase retrieval
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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