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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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Exciting Fully Funded PhD: Computational Modelling for High-Pressure, Low-Carbon Storage Technologies. Be a Key Player in Shaping the Future of Clean Energy Storage! School of Mechanical, Aerospace
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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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to the development of multiscale computational models for simulating crack propagation and establishing reliable methods to predict the residual strength of composite structures. The simulations, performed in Ansys
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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Deadline: 30 September 2025 A fully funded 3.5 year PhD position is available to work on the project titled “Scalable benchmarking for digital quantum computers based on blind testing
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integrating advanced vision and language transformers with an Explainable AI (XAI) layer, the project aims to create a robust system for accurate threat identification, providing actionable intelligence
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relations with non-HEI partner organisations. SEDarc’s vision is to offer a transformative social science training experience that combines leading- edge research skills training, high-quality placements
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The University of Surrey is a global community of ideas and people, dedicated to life-changing education and research. We are ambitious and have a bold vision of what we want to achieve - shaping
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consumables, equipment, and travel related to the project. Number Of Awards ReNU+ is a unique and ambitious programme that will train the next-generation of doctoral carbon champions who are renowned