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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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and transverse motion of quarks and gluons. This information is captured by Transverse Momentum Dependent (TMD) parton distribution functions. While parton tomography is essential for understanding how
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based on seismic noise correlations, particularly for applications to crustal and upper mantle tomography in various regions of the world, notably in the Alps (e.g., Nouibat et al., 2022, 2023
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to the battery and large-scale facility user communities. Develop a characterisation workflow of complementary techniques: combining operando 2DµXRD imaging with in situ µ/nano X-ray Computed Tomography (XCT) and
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spatial and temporal dimensions Quantitative functional lung imaging using near field X-ray speckle Ultra-low dose Computed Tomography for lung, brain or breast imaging. Dark field X-ray imaging Translating
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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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Approach ( https://vhrz669.hrz.uni-marburg.de/ssf/ ). Understanding the mechanisms controlling subsurface flow (SSF) and the conditions under which it occurs remains a major challenge in hydrology and
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interfacial chemistry on the atomic-scale is as yet unresolved, owing to the lack of sufficiently suitable analytical capabilities. In this project, the PhD candidate (m/f/x) will employ atom probe tomography
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analysis in the area of magnetic resonance imaging (MRI) and positron emission tomography (PET); Minimum Level 1 certification in Cardiac Computed Tomography (CT) and/or Cardiac Magnetic Resonance Imaging