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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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should have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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in computer vision would be beneficial but not essential; determination, curiosity, and a willingness to learn are key attributes we value. Applicants with alternative qualifications, industry
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. The successful candidate will develop advanced skills in multi-modal sensor fusion, signal processing, machine learning, and integrity assessment, as well as transferable abilities in critical thinking, project
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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and industry. This is an exciting opportunity for a suitable candidate where he or she will be exposed to the latest technology, learn from experts working in the area and prepare for an exciting career
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. •Specialist training in AI, machine learning, and digital engineering. •Collaboration with academic and industry experts for technical insight and mentoring. •A supportive research environment focused on both
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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future hydrogen fuel cell powered aircraft. Join our diverse and inclusive team to transform the future of aviation as part of the Centre for Propulsion and Thermal Power Engineering. Offering fully funded