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unified artificial intelligence (AI) model capable of segmenting 3D medical images from standard clinical scans and generating 3D meshes across multiple imaging modalities. The project will also investigate
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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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requirement. A very good command of the English language, both written and spoken, is a key requirement. Experience in Federated Learning, Computer Vision, Image Analysis, Mathematics, and Mathematical
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exceeding $2.5 million. Research activities are conducted in multiple research laboratories at the Department and research centers in the College and the University. Job Description: The Department
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geometry altogether and operate in hyperbolic space. Our lab has published multiple papers showing that hyperbolic deep learning has strong potential for computer vision, from hyperbolic image segmentation
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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are a major part in implementing the 2030 vision of the Faculty of Medicine, i.e. to become leading within digital health and well-known for doctors and engineers finding solutions together. Our mission
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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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–plant interactions. Your main responsibilities will include: Developing and applying high-resolution time-lapse GPR and EMI imaging methods at multiple scales to enhance our understanding of the soil–root
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GPR and EMI imaging methods at multiple scales to enhance our understanding of the soil–root system Designing and implementing novel inversion algorithms for GPR and EMI data Identifying links between