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); The applicants may have a background in any aspect of Materials Science, Metallurgy, Physical science or Engineering. A copy of your undergraduate/Postgraduate degree certificate(s) and transcript (s); Names and
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MLPs to run classical MD simulations and characterise thermal transport. This PhD project will be based within the School of Engineering, University of Edinburgh. This PhD project will be supervised by
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This 4 year PhD project is fully funded and home students, and EU students with settled status, are eligible to apply. The successful candidate will received an annual tax free stipend set at
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-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those
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Engineering (PhD) Eligibility: UK Students Award value: Home fees and tax-free stipend £20,780 - See advert for details Project Title: Machine Learning and Optimisation-Based Intelligent Substation Design in
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Applications are invited for a fully funded, full-time PhD studentship in the Department of Mechanical and Aerospace Engineering, supported by Vestas Technology (UK) Ltd, one of the largest wind
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applicants should contact Dr Jiling Feng for an informal discussion. To apply you will need to complete the online application form for a full-time PhD in Engineering (or download the PGR application form
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requires not only quantification of respective changes in materials but also development of novel tools for design and optimisation of new engineering solutions. This will be achieved by combining
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, surgery planning with patient data for surgeons, real-time remote guidance for maintenance in industrial plants, and iterative design simulation for architecture and engineering. However, its wide adoption
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-combustion simulations High-fidelity turbulence simulations (Large Eddy Simulations ) to assess real-world PAC performance Training & Development The PhD student will receive comprehensive training in: Plasma