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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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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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–2 years, total 3–4 years) on deep learning for medical imaging. This DFG-funded project focuses on developing deep learning methods for medical and scientific imaging. The Professorship for Machine
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tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep learning to push our
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tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep learning to push our
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PixHawk Autopilot, Arduino boards, Raspberry Pi - or equivalent Experience with ROS/ROS2 Experience with programming languages like Matlab, Python, C++ Familiarity with machine learning and/or deep learning
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international partners. Become part of our team as PhD student (f/m/d) Work schedule: 65% of the regular weekly working hours Duration: at the earliest possible date; third-party funding until 30 April 2029
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host chromatin pathways (DFG Research Unit DEEP-DV, FOR5200). The group uses experimental infection systems, an array of high-throughput sequencing methods, and single-molecule live-cell imaging
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thesis is not pre-defined and should be defined by the candidate over the course of the first year. The domain of the PhD thesis must be machine learning and either control theory, path planning, or multi