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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
at scale? Digital twins offer a promising foundation, but to truly support engineering decisions, they need to go beyond simulation and begin to interpret and reason about the systems they represent
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Research theme: "AI and Robotics in engineering", "Digital security and resilience", "Digital security and resilience" How to apply:uom.link/pgr-apply-2425 Number of projects: 1 This 3.5-year
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This PhD opportunity at Cranfield University invites candidates to explore the integration of AI into certification and lifecycle monitoring processes for safety-critical systems. The project delves
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challenges in quantum technology adoption stem from the lack of standardized benchmarking methods and the inherent difficulty in validating quantum devices beyond classical simulation capabilities. Recent
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Deadline: 30 June 2025 A fully funded four-year PhD position is available to work on the project titled “Real-world quantum verification and benchmarking of noisy hardware”. This position is a
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the opportunity for the PhD student to lead the development of innovative simulation tools that predict Litz wire behaviour across electrical, thermal, and mechanical domains. Supported by the MTC’s advanced wire
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, there is no consensus on the adsorption mechanisms of these molecules on the metallic surfaces. In this PhD project we will use state-of-art molecular simulation methods [2,3] to clarify the adsorption and
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and establish their potential remain to be answered, both in terms of enabling technologies for distributed sensing and the techniques that exploit it. The focus of the PhD project is to improve our
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. This PhD will be supervised by Dr Enric Grustan (Lecturer, Cranfield University) and Dr Adam Baker (Visiting Fellow at Cranfield and Senior Project Engineer, Magdrive) At a glance Application deadline30 Jul
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how variations in mould structure, porosity, and surface characteristics affect radiative heat transfer and casting performance. Phase-field modelling will also be used to simulate defect formation and