488 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UNIV" "Univ" PhD scholarships in United Kingdom
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measurement and stopping rules controlling type-I error with finite-sample guarantees. Quantum statistical modeling: use of quantum relative entropy, Fisher information, and variational quantum circuits
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are emerging as promising alternatives, offering potential benefits in carbon reduction, durability, and ageing resistance. However, widespread adoption is limited by inconsistent performance data, insufficient
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should hold a Master’s degree in Economics or a related field (e.g., Finance, Psychology, Maths or Data Science), with a foundational understanding of behavioural/experimental methods and/or econometrics
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placement will be provided by HM Inspectorate of Probation. For further information about the application process, please contact the Admissions Team For further information about the project, please contact
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’) as the programme of study You will then need to provide the following information in the ‘Further Details’ section: A ‘Personal Statement’ (this is a mandatory field) - upload a document or write a
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completed application should be emailed to the Doctoral College Admissions team by 2nd March 2026 at 9am. Funding information Full tuition fee waiver for three years and a stipend each year at UK Research
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that deliver healthier indoor environments, lower carbon emissions, and long-term building performance. By integrating Passive House and EnerPHit principles with real building data, the research will support the
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the Faculty of Engineering, plus those housed in Plant Biosciences at the Sutton Bonnington campus. Data sets will be generated using simulated and experimental data and these will be used to train networks
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for candidates who can demonstrate strong research potential. Suitable backgrounds include, but are not limited to: Human Factors Law (particularly law and technology, medical law, or data governance) Psychology
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computational design, Industry 4.0 integration, digital twins, and data-driven optimization to enhance manufacturing efficiency. Working closely with the NWCAM2 companies, this project aims to reduce waste, embed