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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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as structural members when deployed make a good basis for a deployable structure. The two main aims of this project are to develop novel designs and manufacturing methods for foldable composite
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Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering Research Group) Aim: Develop a mathematical model for obsolescence modelling for railway signalling and telecoms
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lead to transformational developments for the chemical industry and associated energy technologies. This project, which is funded by the Royal Academy of Engineering, will involve the selection
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power generation companies—to develop robust methodologies for investigating failure mechanisms in CFRP sleeves. Your contribution will help prevent catastrophic magnet detachment and significantly
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with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
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We are seeking a highly motivated PhD student with a strong scientific background and a desire to develop interdisciplinary research skills to join a cutting-edge project at the intersection
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, which are prone to failure under extreme rotational stresses. This project responds to industrial demand—highlighted by Aerospace and power generation companies—to develop robust methodologies
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simulation study of light matter interaction, digital twin enabled process development and life cycle assessment will be researched. Opens: Immediately Deadline: 08/08/2025. Duration: 36 months Funding: Funded
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reducing the environmental impacts of computational science. The tools and frameworks developed and maintained by the group are used internationally and include the popular Green Algorithms online calculator