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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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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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) and satellite platforms, and surface energy balance models will be used to obtain evapotranspiration (ET); computer vision and machine learning techniques will also be used to identify and count fruits
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Position in XAI with Commonsense Knowledge for Robotics and Computer Vision 2. PhD Position in Sustainable AI for Enhancing Health Informatics (Please scroll down to read more about the project descriptions
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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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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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, AI-enhanced simulations, and computational workflows to explore biological complexity and design solutions for a sustainable bioeconomy. The four PhD projects are: Further details on each project can
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learning, offers powerful solutions to automate these tasks and provide reliable real-time information. This doctoral project is part of a 5-year research chair on Computer vision applied to the swine sector