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the molecular structural dynamics of proteins and small molecules. We primarily use time-resolved X-ray scattering and diffraction experiments at free-electron laser sources. Our research combines biochemistry
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data (e.g., synchrotron X-ray microtomography), Design and use of autoencoders (VAEs, GANs), diffusion models, and other ML methods for analyzing and discovering patterns in probability distributions in
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methods for optimized data analysis, Machine learning-based image segmentation of tomographic data (e.g., synchrotron X-ray microtomography), Design and use of autoencoders (VAEs, GANs), diffusion models
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. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR imaging and spectroscopy) to evaluate performance of electrodes and to map electrolyte chemical composition in
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and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR
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Electron Microscopy (TEM) and HAADF-STEM images. Microscopy data are often degraded by noise and scan distortions, and clean ground truth data are rarely available. This project aims to go beyond standard
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application! At the intersection between AI and single atoms. Your work assignments We are looking for a PhD student with a background in machine and deep learning with focus on image processing and restoration
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focus on image processing and restoration, to develop novel AI-based approaches to restore and denoise Transmission Electron Microscopy (TEM) images. This position is part of a cross-disciplinary research
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forces. The quantum sensing advantage arises from uniquely quantum resources such as coherence and entanglement, as well as from how the parameter of interest is encoded into the quantum state. Candidate
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at the same time so special. The originality of the experiments is in the combination of X-ray based scattering and imaging methods to monitor the changes at the particle scale during testing. Research