466 development-"https:"-"https:"-"https:"-"Institut-Agro-Rennes-Angers" PhD positions in United Kingdom
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Field
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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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PhD Studentship: Robust quantum control for quantum error correction The development of fault-tolerant quantum computing is one of the most coveted aims of quantum technology. It will bring about a
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develop advanced skills in qualitative research, stakeholder engagement, and knowledge exchange, alongside being supported to publish in leading journals and present at conferences. You will also benefit
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relying on incremental optimisation of existing materials. By developing novel multilayer dielectric materials with ultra-high breakdown strength, the research will revolutionise electrified technologies
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health. This is an exciting opportunity to work in a high profile, a pioneering initiative to develop novel built environment models to inform health outcomes. HD4 is a major international collaboration
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the operational life of battery-powered devices and reducing the environmental impact of large-scale deployments. Advancements in this area support the development of sustainable technologies across various
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one of the world’s leading centres for additive manufacturing research and development, invites applications for a fully funded PhD programme. Metal additive manufacturing is transforming how complex
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Dalton Building at Manchester Metropolitan University. You will develop skills in computational experimentation, financial data analytics, and systemic risk modelling, with extensive opportunities
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and identify structural barriers to nutrient release. Drawing on these mechanistic insights, you will also help to develop and test prototype nutritional supplements or modified food‑structure
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this project, you will develop the next generation of federated machine unlearning algorithms—methods that can efficiently deliver genuine, verifiable, and robust erasure without sacrificing model performance