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Field
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. In this project, you’ll have the opportunity to be trained and become a proficient user of a range of advanced experimental techniques. For instance, you’ll learn how to use in-situ X-ray Computed
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their swimming dynamics and the mechanical deformations caused by the encapsulated active biomolecules, you will explore ways to control their motion in 3D space. Synthetic microswimmers have many potential
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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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. 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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mechanics, acoustics, and computing science. It will potentially improve our current understanding of the silent flight of owls by uncovering the full mechanisms of noise reduction by flexible trailing edge
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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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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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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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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
) in a relevant discipline such as aerospace engineering, mechanical engineering, electrical engineering, computer science, applied mathematics, or a closely related field. Experience or interest in
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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