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Description For our location in Hamburg we are seeking: PhD Position: Method Development for Hierarchical X-Ray Imaging at Synchrotron Facilities Limited: 2 years | Starting date: earliest possible
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students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position A PhD position, fully funded for three years
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7th January 2026 Languages English English English The Department of Biology has a vacancy for a PhD Candidate in Plant Evolutionary Ecology: plant-parasite interactions under climate warming Apply
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into the generation process. This multidisciplinary project will deliver deployable models, reproducible methods, and, where allowed, shareable datasets. The student will gain training in deep learning, AI, image
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learning has strong potential for computer vision, from hyperbolic image segmentation [2] to hyperbolic tree embeddings [3] and hyperbolic vision-language models [4,5]. [1] Nickel, Maximillian, and Douwe
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organizational levels of the brain – from molecular and cellular processes to complex neuronal networks and behavior. In association with the SFB 1436, Neural Resources of Cognition (supported by the German
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, MONAI) Strong interest in image analysis / computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not
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students only Entry date: 12th January 2026 Closing date: 4th December 2025. Interviews will be held via MS Teams on Monday 15th December. We are looking to recruit a high-quality PhD candidate
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or equivalent Specific Requirements Item 01: Academic diplomas (degree and master), related to data & image processing (punctuation: 25) Item 02: Enrolled in a PhD program related to Applied Geotechnologies
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PHD DEVELOPMENT AND EXPERIMENTAL VALIDATION OF UNDERGROUND SAND-BASED THERMAL ENERGY STORAGE SYSTEMS
cycles. You will contribute to the design, construction, and operation of an upscaled SMTES prototype (20-foot container scale) to investigate realistic thermal behavior, temperature stratification, and