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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based
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. Although there is a clear synergy between fatigue damage and corrosion, most fatigue prognosis models do not explicitly consider the role of the environment, which is usually reduced to obscured fitting
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landscapes and emerging stressors. With opportunities for field or semi-field studies and close collaboration with leading academic and industry partners, this project is ideal for candidates who want
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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing
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advanced technology and business needs, creating smart monitoring systems, predictive maintenance solutions, and digital twins that solve pressing challenges across healthcare, energy, aviation, and
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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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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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This project is sponsored by EPSRC, Cranfield University and a consortium led by ETN global. ESPRC will provide stipend (£22,000 per annum) and cover UK fees. The consortium will provide supervision
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Early and accurate cancer detection is a major global healthcare challenge, with significant implications for patient outcomes and treatment strategies. Time-of-Flight Positron Emission Tomography
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Nuclear fusion offers the prospect of clean, abundant, and safe energy that could transform global energy systems. Achieving this goal depends on materials that can endure extreme environments