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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
knowledge graphs for predictive insights. Design feedback mechanisms to deliver interpretable outputs such as alerts, recommendations, and confidence scores. Validate system robustness using real-world
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1st class degree in Engineering with strong background in theoretical and computational mechanics of solids/materials. To apply please contact the supervisor, Prof Andrey Jivkov - andrey.jivkov
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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experimental mechanics, classical materials testing also benefits from a range of well-established methods. However, biomedical applications lack such a wealth of standardized protocols to address the diverse
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of the mechanical properties of the cellular actin cortex, the biomechanics of cell division, and the coupling between cell shape and mechanics and cellular state / fate during cellular transitions. The successful
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. The overall aim of this PhD project is to analyse droplet impact mechanics along with the freezing thermodynamics under high airspeeds to gather important insights into ice adhesion behaviour. The experiments
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Engineering, Mechanical Engineering, Aeronautical Engineering, Automotive Engineering or other relevant Engineering and Science subjects, or relevant industrial experience. English language requirements
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to study corrosion, cracking and mechanical degradation, develop advanced computational models using modern C++ and high-performance computing to simulate material behaviour over a 100+ year timespan. This
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may be possible, please contact Dr Mark Whiting once deadline passes. You will need to meet the minimum entry requirements for our Engineering Materials PhD programme. Candidates must meet Surrey
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. The integration of AI into hardware not only enhances performance but also reduces energy consumption, addressing the growing demand for sustainable and efficient computing solutions. This PhD project delves