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A 3.5 year PhD Engineering studentship under the supervision of Dr Evgeny Petrov . Blade vibrations in compressor and turbine bladed disks are one of the critical issues in designing gas-turbine
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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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factors (learning curve). These will be linked with the CCTA imaging biomarker data (quantitative plaque analysis) that is being collected as part of the EU HORIZON TARGET study to determine personalised
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Human Behaviour in projects targeting social good. Research at N/LAB focuses on the development and application of innovative computational methods using Big Data, Behavioural Science and Machine Learning
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dissertation supervision. For this T&S role, you must have a PhD in a relevant subject (or a PhD near completion – i.e. strictly within three months of submission), and a strong scholarly profile or trajectory
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untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis
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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This
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Exciting Fully Funded PhD: Unlock the Future of Turbo Gas Seal Technology with John Crane Ltd! School of Mechanical, Aerospace and Civil Engineering PhD Research Project Competition Funded Students
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the Uehiro Oxford Institute. This role would be suitable for an individual who would like to gain research experience prior to applying for PhD study. About us Based in Littlegate House, the Uehiro Oxford
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As part of the Restoration Ecology And Dynamics (READY) Doctoral Focal Award, we invite applications to the following PhD project: Harnessing ecosystem resilience to inform woodland restoration