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courses on advanced semiconductor technologies Design pathfinding PDKs as learning assets Interuniversity research programs across Europe 🔬 Nano IC-related PhD topics include: Machine-learning for epitaxy
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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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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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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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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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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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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
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combines: Fluid dynamics and heat transfer (theory and experiments), Computational modeling, and Machine learning / computer vision for data analysis and pattern recognition. The goal is to improve
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. dos Santos is an Assistant Professor (Lecturer) in Computer Vision at the University of Sheffield. His research interests include remote sensing image processing, computer vision and machine learning
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Machine Learning, and has over 30 PhD students, postdoctoral researchers and faculty members working on a broad variety of deep learning, computer vision, and foundation model subjects, like self-supervised