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Field
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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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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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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
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: • Image processing and sensor data analysis, ideally with HSI or other imaging technologies. • Machine learning and artificial intelligence, especially classification and segmentation tasks. • High
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of translation is crucial for controlling protein levels and guiding development. In the Ruijtenberg lab, we study these mechanisms using a combination of genome-wide sequencing and single-molecule imaging
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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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reconstruction, as well as in situ electron tomography approaches to image bacterial multi protein complexes, including cytoskeleton proteins and the mechanisms regulating bacterial polarity and cell division
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electron tomography approaches to image bacterial multi protein complexes, including cytoskeleton proteins and the mechanisms regulating bacterial polarity and cell division. Qualifications To be admitted