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Early-stage failure prediction in fusion materials using machine learning
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Autonomous Industrial Perception, Monitoring, and Optimisation Using an Agentic Framework with Large Language Models
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). • Eligibility: First degree and Masters in one of engineering and computing fields • Standard departmental requirements: First Class • Experience in physical modelling and machine learning, interest in medical
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. Experience required: 1st or 2:1 degree in Engineering, Physics or other relevant discipline. Experience in computational modelling would be a benefit. Funding Notes This is a self funded project. View
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and internationally. More information on the School can be found here Main duties and responsibilities Research Determine research objectives, and initiate and implement a programme of research that
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members of the research team and agree on an inclusive strategy to complete the research programme. Regularly update the research team on project progress and development. Support and maintain a collegiate
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, and we encourage you to apply. Please ensure that you reference the application criteria in the application statement when you apply. Essential Criteria A PhD (or equivalent) in Information Science or a
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the validity of the relevant literature and its contribution to the development of own field. Determine novel research objectives as experiments progress and initiate and implement relevant programme of research
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Details The project will advance the accuracy of Computational Fluid Dynamics (CFD) models employed for predicting boiling heat transfer and particularly the Critical Heat Flux (CHF) in nuclear Pressurized
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societal costs. Recently, computational models based on in vivo microCT images have shown high potential to assess the biomechanical properties of bones. In this project, we will aim to show that microCT