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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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scholarship is suitable for students with a background in Engineering, Mathematics, and Computer Science. Students with interests in machine learning, deep learning, AI, intelligent decision making
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PhD Studentship: Artificial Intelligence for Building Performance – Optimising Low-Pressure Airtightness Testing Supervisors: Dr Christopher Wood (Faculty of Engineering) and Dr Grazziela Figueredo
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mechanisms , smart electroactive materials , embodied intelligence , advanced control systems , and microfabrication techniques . This PhD forms part of the new £14 million VIVO Hub for Enhanced Independent
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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October 2026 start ONLY For January and April starts please use the relevant application. This form is only to be used by those self-funded applicants seeking a place on a research degree programme at
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strong industry partnerships, attracting top-tier students and experts globally. As an internationally recognised leader in AI, embedded system design, and intelligent systems research, Cranfield fosters
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collaborations with industry giants including Boeing, Rolls-Royce, Thales, and UKRI, this research offers a unique platform to contribute to the advancement of intelligent assurance methodologies in sectors like
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on performance and safety, for example, through the efficient computation of Lyapunov and barrier functions, forward and backward reachable sets, optimal value functions etc. The broad goal is to build upon recent
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Sh.24.3 PhD in Sound Analysis for Predicting Category 1 Ambulance Calls School of Computer Science PhD Research Project Competition Funded UK Students Dr Ning Ma, Prof Jon Barker Application