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reduction (MAR) algorithms, AI-based segmentation, and automated 3D anatomical modelling, promise clearer, more reliable imaging. Integrated effectively into clinical workflows, these advances have the
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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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nanotechnology, with access to state-of-the-art instrumentation for HAADF-STEM imaging and in situ experiments. The project will be pursued in close collaboration with the Materials Design Division (also at IFM
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to advance 3D imaging methods for neuroscience. Your colleagues: An interdisciplinary team working across the Cognitive Neuroscience Department and the Mental Health and Neuroscience Research Institute
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. Besides this, you will work on scene understanding using RGB and possibly thermal and radar images, including based on object detection and image segmentation, and collaborate effectively with other
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for non-invasive brain imaging techniques such as fNIRS or fMRI. With the constantly improving spatial resolution of these methods, a thorough knowledge of potential differences in vascular architecture
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will work on scene understanding using RGB and possibly thermal and radar images, including based on object detection and image segmentation, and collaborate effectively with other technical partners who
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and possibly thermal and radar images, including based on object detection and image segmentation, and collaborate effectively with other technical partners who will work with UAV-based radar systems
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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