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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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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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consortium, 23 partners across Europe, aims to unlock the hidden potential of global metagenomic sequence space using a combination of synthetic biology, machine learning (ML), and ultrahigh-throughput
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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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. 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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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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electromagnetic device performance analysis>>>Embedded SystemsResearch focuses on:•Robotics, computer vision, and machine learning/deep learning•Wearable and implantable sensors, biosensors, and tele
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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large