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Primary supervisor - Dr Myriam Charpentier Are you passionate about plant biology, endosymbiosis, system biology or bioengineering? Join a cutting-edge research project exploring how plants
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the Crick’s clinical PhD programme. About the Fellowships: This fully funded three-year PhD programme offers talented clinicians the opportunity to pursue biomedical discovery research in a world-class research
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applicants may require an ATAS (Academic Technology Approval Scheme ) clearance certificate prior to obtaining their visa and to study on this programme. How To Apply Please read and complete this document as
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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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. You will be part of an active research programme in the Heat and Fire Lab (https://heatandfire.github.io/ ) in the Department of Engineering under the supervision of Dr Francesco Restuccia . Our group
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Research Studentship in Engineering 3.5-year D.Phil. studentship Project: Electronic-free Mechanical Oscillators for Fast Locomotion in Soft Robots Supervisors: Prof Antonio Elia Forte Soft robots
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21 Oct 2025 Job Information Organisation/Company The Institute of Cancer Research Research Field Biological sciences » Biology Mathematics » Computational mathematics Researcher Profile First Stage
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by researching and learning abroad as part of your PhD programme Preparation for a prestigious academic or research career Option to choose our Behavioural Science pathway. Course details: The MRes/PhD
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: At least an upper second-class degree (preferably MSc) in a Science or Technology discipline. Good working knowledge of machine learning and deep learning. Hands-on knowledge of Python or PyTorch
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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