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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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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 13 days ago
dynamics data and advanced graph-based deep learning models to decode long-range communication pathways within macromolecular complexes. The PhD candidate will play a central role in this effort by
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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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Experience with MATLAB/Simulink, including Control System Toolbox, System Identification Toolbox, or Deep Learning Toolbox. Understanding of battery systems, electrochemical energy storage, or battery
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develop automated pipelines for segmenting and analysing X-ray computed tomography (XCT) images of titanium layers. This includes deep learning-based image analysis and the extraction of quantitative
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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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(e.g. computer vision, deep learning, AI) and green life sciences (e.g., remote sensing, crop modelling, and food security), within the European funded project AgriscienceFM (Horizon programme), which
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, machine learning techniques, etc.) is desirable. This thesis offer within the AstroParticle and Cosmology Laboratory (APC) is part of the Deep Underground Neutrino Experiment (DUNE). DUNE is an
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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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environment. Key research Objectives: AI Innovation (Taxonomic Identification): Developing and optimizing deep-learning architectures (e.g., YOLO) for the automated detection and classification of nocturnal