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. A policy internship at a relevant organisation is embedded into this programme. Number Of Awards One Start Date 5th January 2026 Award Duration 3.5 Years Application Closing Date 30th September 2025
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, operational reconstructions of quantum theory, indefinite causal structures, extended Wigner's friend scenarios, and quantum reference frames. The team is embedded within the Quantum Optics, Quantum Nanophysics
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, operational reconstructions of quantum theory, indefinite causal structures, extended Wigner's friend scenarios, and quantum reference frames. The team is embedded within the Quantum Optics, Quantum Nanophysics
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2025 Reference: RD-PHD-02-LS-MH-25 Project Title: Dietary strategies to reduce methane production in dairy cows and their effects on the rumen microbiome and metabolism Primary supervisor: Prof. Liam
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We invite applications for a fully funded PhD studentship (3.5 years) hosted by the University of Birmingham and conducted in collaboration with the UK Met Office. This project is ideal for
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. Funding This is a self-funded PhD. Find out more about fees. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and
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2025 Reference: RD-PHD-01-LS-MH-25 Project Title: The cost of silage production: measuring the carbon footprint of silage Primary supervisor: Prof. Liam Sinclair Co supervisors: Dr Kate Le Cocq, Dr
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electronics, embedded programming, signal processing, vibration measurement and analysis, maintenance engineering, and electro-mechanical engineering. Funding This is a self-funded PhD. Find out more about fees
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We are seeking a highly motivated candidate to undertake a PhD program titled "3D Temperature Field Reconstruction from Local Temperature Monitoring in Directed Energy Deposition." This exciting
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
predictive and explainable digital twins. The core challenge this PhD will tackle is how to help digital twins make sense of complex, messy maintenance data and turn it into clear, useful insights