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Field
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treatment that safeguards microbiological quality and boosts resilience to climate uncertainty. This three-year PhD will deliver the first full-scale evaluation of robotic maintenance for slow sand filters
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across both physical and digital technologies, to deliver a cyber-physical platform for the autonomous inspection, digital representation, and maintenance of highways. The successful candidate joins a
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
for training and conferences, and includes a placement with Rolls-Royce. This project focuses on advancing digital twins with AI-driven reasoning for predictive maintenance in aerospace systems. While aircraft
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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Salary The studentship will cover full tuition fees at the Home (UK-based) student rate and a maintenance grant for 3 years, at the rate of £21,237 pa. A funded PhD position is available on a
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PhD Studentship: Multi-robot Cyber-physical Systems for Solar Farm Health Monitoring and Maintenance
be how RAS can be used for the inspection, maintenance, and repair of new infrastructure in renewables (wind, solar, geothermal, tidal, hydrogen) and nuclear (fission and fusion), and to support the
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Join us for an exciting and excellent PhD journey to create the future of maintenance! Explore the fascinating future of Net Zero industries where complex and highly automated equipment need
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offshore wind OEM—invites applications for a fully funded PhD position on the “Development and Implementation of an Autonomous Decision Support System for Optimized Maintenance in Wind Turbine Infrastructure