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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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to begin focused research early on. To complement their background, optional training in natural language processing, machine learning, and linguistics will be available. During the first year, the student
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patient outcomes. This PhD studentship is expected to deliver the following outcomes: A validated protocol for at-home hormone monitoring using the Unravel Mini-Lab in the context of menopause. Demonstrated
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autonomous by embedding machine learning algorithms to search through different reaction parameters Person Specification Candidates should have been awarded, or expect to achieve, EITHER: A Bachelors degree in
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Overview Location: College Lane Campus Programme Description The Centre for MoDDD at the University of Hertfordshire invites applications for a PhD candidate to join a 3-year QR funded research
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DPhil Studentship in Physical Chemistry Topic: Electron Spin Resonance (ESR) spectroscopy applied to energy materials Supervisor: Dr Claudia Tait Start date: 1st October 2025 Applications are invited for a DPhil studentship in Physical and Theoretical Chemistry, available from October 2025, to...
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. The research will combine computational modelling, experimental validation, and machine learning techniques to develop a predictive phenomenological PAC model. The successful applicant will develop and apply
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We invite applications for a fully funded PhD studentship (3.5 years) hosted by the University of Birmingham and conducted in collaboration with the UK Met Office. This project is ideal for
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of Strathclyde will lead the wind energy training and research elements of the programme. Funded by EPSRC, this 4 year PhD studentship, at the University of Strathclyde is in the area of novel wind turbine concept
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This PhD project aims to address one of the key challenges in the manufacturing industry, the increase in productivity by utilizing the equipment with its optimum performance and without any