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Field
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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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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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, Continuum Mechanics, Computer Science, Materials Science, Physics or a related subject, or relevant industrial experience. English language requirements: Applicants must meet the minimum English language
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in a degree, ideally at Masters level, in an Engineering subject, Physics, Mathematics, Computer Science or other quantitative background. Knowledge in fluid mechanics, ocean waves, numerical methods
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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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This self-funded PhD research project aims to develop smart sensors based on low-frequency resonance accelerometers for condition monitoring of ultra-speed bearings. The developed smart sensors will
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Testing (NDT) – a crucial technique used in industries to assess materials, components, and structures without causing damage. This PhD project focuses on advancing Ultrasound Testing and X-ray Computed
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an objective and consistent method of measurement. The implication of this is a mechanism through which customer and supplier can agree on model quality. Furthermore, the outcome will inform the development
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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