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Field
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the risk of missed defects. Using the power of Artificial Intelligence (AI), this research aims to: Automate defect detection in complex 3D structural data Enhance diagnostic accuracy and processing speed
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(complexity). Develop and optimise a modelling pipeline including a decision support dashboard for optimal patient selection for surgery to ensure daily surgery caseload optimisation, post-operative care
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resources efficiently, and clearly convey complex information. Previous experience with decarbonization in the transport sector, LCA, EE-MRIO models, sampling design, and emerging cities including working
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is characterised by complex and highly dynamic turbulent flows that define the performance and design of renewable energy systems and their infrastructure. This PhD project aims to enhance
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shift in the world of hardware design. On the one hand, the increasing complexity of deep-learning models demands computers faster and more powerful than ever before. On the other hand, the numerical
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can be varied. Crucially, the models we derive will be validated by real-world measurements to ensure our simulation environments are realistic and scalable to more complex radar networks. This will
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to ensure AI models deliver reliable, transparent, and auditable decisions in complex industrial contexts. This project offers an exciting opportunity for you to shape the next generation of industrial AI
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Modelling post combustion amine CO2 capture plant School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof Mohamed Pourkashanian, Prof Lin Ma, Dr Kevin Hughes
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community-led model. Investigate how knowledge is co-created and used across different scales (individual, organisational, systems). Compare the Isles of Scilly CRN with eight other CRNs across the UK, each
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vulnerabilities. Frontier models show superior performance when combined with a focused knowledge base and multi-agent architectures. However, in most cases human involvement is still required, and fully autonomous