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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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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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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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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 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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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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farms at relatively close proximity can be relevant when considering their annual energy production. This project will examine the uncertainty of various types of numerical models, from fast-computing
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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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the above ‘Apply’ button. Under Campus, please select ‘Loughborough’ and select the Programme “Mechanical and Manufacturing Engineering”. Applications must include a CENTA studentship allocation form , a
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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