568 data "https:" "https:" "https:" "https:" "University of Aberdeen" PhD positions in United Kingdom
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(Honours) 2:1. English Language requirement (MED/SCI equivalent: IELTS 6.5 overall, 6 in each category). Mode of study Full-time Start date 1 October 2026 Additional Funding Information This project is
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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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The minimum entry requirement is 2:1 (any degree with a substantial biological sciences component). Mode of study Full-time Start date 1 October 2026 Additional Funding Information This project is fully funded
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manufacturing sectors, from SMEs to large global manufacturers. For details, visit the MTC website . Entry requirements: A 1st or high 2:1 degree in computer science, manufacturing/industrial engineering, data
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how large language models can generate goal-oriented robotic programs from natural language task descriptions and contextual information. Developing agentic AI architectures that allow robots to plan
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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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computer simulations by developing fundamentally innovative and advanced protection strategies. To enhance the reliability and safety of low-voltage networks with a high penetration of power-electronic
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qualification). Further information Informal enquiries to Dr Beth H. Richardson (bhrichardson@lancashire.ac.uk ) For full details: Apply here: online application system . Quote reference NWSSDTP CASE Studentship
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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