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) Eligibility: UK Students Award value: Home fees and tax-free stipend £20,780 - See advert for details Deadline: 31st July 2025 Project Title: Thermal Energy Decarbonisation of Large Industrial and Commercial
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conference presentations. Funding This is industry funded project, funded by two big aerospace primes. Funding will cover tuition fees, plus a stipend based upon the current Research Council rate of £20,780
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such approaches would be cost effective. To achieve this, you will be supported to undertake analyses using large data registries such as the Clinical Practice Research Datalink. This is an exceptional opportunity
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and colleagues located across the 4 nations of the UK. HDR UK’s mission is to unite the UK’s health data to enable discoveries that improve people’s lives. Its 20-year vision is for large-scale data and
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. when do we stop modelling? How do we track / score the quality of the model? What is the required level of quality over time? How can quality be brought to the required level? Can Machine Learning, Large
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
intelligent reasoning and feedback mechanisms into digital twin environments, enabling them to interpret complex maintenance data more effectively. Using AI techniques, such as large language models, knowledge
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generations of research and development professionals, data specialists, technology experts, inventors, and scientists for industry and society. The Macroscopic Quantum Optics (MQO) Group at the Department
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of challenges of building large-scale systems. Programming skills in Python. A good Bachelor’s Hons degree (2.1 or above or international equivalent) and/or Master’s degree in a relevant subject (physics
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links between home and flexible working patterns, mobility patterns and wellbeing. The project will take a multidisciplinary approach combining quantitative analysis of large-scale secondary datasets
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, machine learning, and information-theoretic approaches to achieve robust, non-intrusive security for the ever-expanding IoT landscape. Feature Engineering for Encrypted Traffic: It is crucial to identify