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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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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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. Cranfield University is a world-leading postgraduate institution renowned for its applied research and deep industry connections, particularly in aerospace, defence, and security. Its Centre for Electronic
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provides a dynamic and applied research environment, ideal for students seeking to pursue careers in food safety, public health, or analytical sciences. In addition to gaining deep domain knowledge in food
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
relationships, together with deep domain expertise. These methods open new possibilities for extracting and connecting knowledge at scale. The goal is to enhance digital twins with the capability to interpret
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training programme in respect of industry-specific skills, and access to hotfire facilities at Westcott, Machrihanish, and elsewhere. You can learn more about the programme at r2t2.org.uk. Kick stages are a
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, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical expertise, enhancing their research capabilities and employability in
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skillset, deep industry insights, and a commitment to sustainability, fully prepared to drive the decarbonisation of aviation in diverse careers spanning industry, academia, government, and policymaking
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learning’ approaches (such as Deep CNN’s) and ‘unsupervised learning’ approaches (such as reinforcement based learning and generative adversarial networks). Some of the main problems with Second Wave AI
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling