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research activities. The applicant will have obtained or be close to obtaining a PhD in Mechanical Engineering or Materials Science. Appointment at Research Associate level is dependent on having a PhD
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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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are demonstrated through its extensive MSc and PhD research initiatives and its ongoing technology development programs in large-scale additive manufacturing. This project will be closely aligned with the ATI
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of Oxford. Unpaired electron spins are ubiquitous in materials and devices for optoelectronics and solar energy technology and play a crucial role in the fundamental photophysical processes at the basis
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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 or another relevant field applicable to the measurement technology development. For this position, we are unable to consider significantly different backgrounds, such as biology- and simulation
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This PhD opportunity at Cranfield University explores how next-generation AI models can be embedded within resource-constrained electronic systems to enable intelligent, real-time performance
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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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); 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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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