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. The project is sponsored by Constellium, a global leading manufacturer of high-quality technically advanced aluminium products and systems. PROJECT Successful applicants will receive an annual stipend
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
Overview: Cranfield University invites applications for a fully funded 3-year PhD, supported by the EPSRC DTP and Rolls-Royce. This studentship covers tuition, a tax-free stipend, funding
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Information Department contact for academic-related enquiries Dr. Johny Daniel Ph.D. Associate Professor School of Education Durham University Johny.r.daniel@durham.ac.uk Contact information for technical
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include simulation and control implementation, data analysis, and knowledge dissemination through technical documentation and stakeholder workshops. This is a full-time, fixed-term role starting 1 September
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LSE is committed to building a diverse, equitable and truly inclusive university Salary from £36,008 to £40,991 pa inclusive with potential to progress to £43,854 pa inclusive of London allowance
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contact Professor Hugh Hunt (hemh1@cam.ac.uk ) for enquiries of a technical nature and Liz Howard (div-c@eng.cam.ac.uk ) for queries related to the application process. Please quote reference NM46702
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
Fully funded PhD at Cranfield University, supported by the EPSRC DTP and Rolls-Royce. This 3-year project covers tuition fees, a tax-free stipend, and funding for training, conferences, and a
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obtaining) a PhD in Chemistry, Materials Science, Engineering, Physics or relevant technical discipline. To perform the role, the applicants will additionally need to have excellent presentation, technical
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tools (e.g. Flow-3D), and keen to develop both technical and communication skills. Funding and Benefits The studentship includes: Full tuition fees A tax-free bursary of up to £25,000/year Access
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integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in