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of complex metagenomic data. You will also become proficient in lifecycle carbon accounting and data-driven decision-making, all mapped to the Researcher Development Framework at Cranfield University. Regular
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We invite applications for a self-funded PhD to explore innovative research in the development of human-centred embodied multi-agent systems that able to compensate and augment human capabilities in
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project will develop novel methods for modelling and controlling large gossamer satellites (LGSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. The candidate will
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focuses on developing an innovative ground-based robotic inspection system using thermographic Non-Destructive Testing (NDT), a critical method for ensuring aircraft safety and reliability. NDT is
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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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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. This PhD project will tackle that challenge by developing intelligent methods that combine AI techniques such as language models that interpret technical text and knowledge graphs that map engineering
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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This self-funded PhD opportunity is open to both UK and international students with a strong background or willingness to develop expertise in offshore engineering, human factors, digital twin
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vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable
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to develop enhanced NbS strategies that target micropollutant removal and remain compatible with other ecological and environmental benefits. The aims of this project are therefore to 1) benchmark the long