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for automatic segmentation and morphometry of histological images; - Compare the predictive value of AI-driven image analysis with clinical and biomarker data; - Collaborate with international experts in medical
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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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using deep learning and AI-driven image analysis. You will: - Analyse pre-implantation kidney biopsies according to the Banff criteria; - Apply AI methods for automatic segmentation and morphometry
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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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, or erroneous data, Data cleaning and generation, Development of enhanced loss functions and information-theoretic methods for optimized data analysis, Machine learning-based image segmentation of tomographic
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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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the Multiscale Imaging of Brain Connectivity section (CBClab) within the Department of Cognitive Neuroscience (Faculty of Psychology and Neuroscience, Maastricht University), and in the division for Neuroscience
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THR demand in younger patients expected to increase fivefold by 2030, revision surgeries will also rise. To improve implant positioning, image-guided navigation is increasingly used in complex THR