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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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Deadline: 30 June 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”. This position is
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is in collaboration with a commercial funder, there will be an expectation that the candidate engages with the company for updates and scientific discussions. They should therefore also have a passion
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Arrangements: This is an in-person role based in Durham, requiring physical presence on campus and regular travel to partner schools in the region to support intervention delivery. The part-time hours (10 per
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, Oxford, OX2 6GG as your normal place of work but you will be able to agree a pattern of regular remote working with your line manager. The position is offered full-time and is funded by various National
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The Engineering Hydrogen NetZero (EnerHy) CDT is an EPSRC-funded Centre for Doctoral Training (CDT) focusing on research and training in the areas of Hydrogen and Wind Energy. The University
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A PhD studentship is available to work on Logistics automation. The student associate will work in the Intelligent Logistics Group within the Distributed Information and Automation Laboratory (DIAL
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The Structural Battery Company, a high-tech manufacturer of EV batteries. Building on Cranfield’s previous APC-funded CERABEV successes using epoxy-based systems with intumescent ceramic phases, this project
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A PhD studentship is available to work on Logistics automation. The student associate will work in the Intelligent Logistics Group within the Distributed Information and Automation Laboratory (DIAL
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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