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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
relevant field such as engineering, computer science, or applied mathematics. Experience or interest in AI, machine learning, or digital systems is beneficial. We welcome candidates from diverse backgrounds
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failures before they occur, enabling proactive maintenance strategies. Anomaly Detection Mechanisms: Implement machine learning techniques to identify and classify anomalies in electronic systems, enhancing
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to achieve, at least a 2.1 honours degree or a master’s in a relevant science or engineering related discipline. Applicants should have strong background in Machine Learning and Deep Learning. To apply, please
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging
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environments such as low light, heat haze, and adverse weather is significantly difficult. These conditions not only degrade video quality but also complicate interpretation by humans and machines, making post
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science, along with proven skills in prototyping software using real-time 3D engines and implementing machine learning models. With 50+ researchers and PhD students, the Centre for Sustainable Cyber Security (CS2
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Technology. Mr Kumar is the module leader for Military Vehicle Dynamics, part of the Military Vehicle Technology MSc, which he teaches in the UK and overseas. He worked on project from the UK Ministry of
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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Machine Learning-based diagnostics and prognostics digital twin system will be developed, aiming to provide fast and reliable predictions of the health of gas turbine engines. Non-confidential operational
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy