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Field
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Project details Early detection of cognitive decline allows identification of those at high risk of developing dementia when medical treatments may be effective in preventing disease onset. Our
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optimisation/high-value products Project start date: 01 October 2025 Supervisors: Dr Steve Slocombe and Dr Miguel Lurgi Aligned programme of study: PhD in Bioscience
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fuel, but its high reactivity makes it vulnerable to pre-ignition. The presence of lubricating oil droplets can worsen this risk by evaporating, altering chemical pathways, and producing nanoscale soot
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such as transportation and heating. These two transformations create a need for sophisticated planning methods and processes that result in actionable plans by proactively considering high-impact forces
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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Research Group at the Faculty of Engineering which conducts cutting edge research into experimental and computational heat and mass transfer, multiphase flows, thermal management, refrigeration, energy
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, balancing efficiency and sustainability in AI deployment poses a significant challenge, calling for advances in model design and training to reduce environmental impact while maintaining high performance
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to applicants with published research or exceptional academic performance. Experience in computer vision or audio processing is desirable and will be considered an advantage. A doctoral candidate is expected
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, requiring large computational effort to assess and study system stability. This is becoming even more challenging under increasing complexity requiring detailed dynamical models and with new dynamic phenomena
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to study corrosion, cracking and mechanical degradation, develop advanced computational models using modern C++ and high-performance computing to simulate material behaviour over a 100+ year timespan. This