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related disciplines Quantitative imaging, data analysis, or computer vision Numerical modeling of biological systems or continuum mechanics Machine learning/AI, particularly explainable AI (XAI) Hands
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Kontogianni. Our research explores how intelligent systems can perceive, understand, and interact with the 3D world. We develop new methods in computer vision, machine learning, and multimodal 3D
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environment with more than 70 researchers active in the fields of computer graphics, computer vision, image processing, visualization, virtual, mixed and augmented reality, and machine learning for visual
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. The vision is that mobile communication services will be seamlessly available to anyone with a 6G device, anywhere and at any time. We are looking for a motivated and innovative PhD student to join our
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seismicity in the area at unprecedented resolution. Leveraging and improving state-of-the-art machine learning techniques, template matching and other techniques, you will derive a high precision catalogue of
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At the Faculty of Medicine, Department of Health Science and Technology, one or more PhD stipend’s are available within the doctoral programme: Biomedical Engineering and Neuroscience from February
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One fully funded, full-time PhD position to work with Alessandro Suglia in the Embodied, Situated, and Grounded Intelligence (ESGI) group at the School of Informatics, University of Edinburgh
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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Requirements Master’s degree (≥4 years) in Computer Science, Informatics, Engineering, Mathematics, Physics or equivalent; PhD in Computer Vision, Artificial Intelligence, Machine Learning, and Data Science, or
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Biological Learning Machine, which is headed by Professor Jan Østergaard. The goal is to develop novel information-theoretic methods for identifying and analyzing temporal and spatial patterns of synergy and