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Field
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the effectiveness of an audio/visual online coaching programme that leverages digital health technologies as part of a novel intervention that can be easily accessed by a wide audience. This will be compared with
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student. We require that applicants are under no restrictions regarding how long they can stay in the UK. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
datasets with industry-aligned performance metrics. The successful candidate will join the Centre for Digital and Design Engineering (CDDE), part of Cranfield’s Manufacturing, Materials and Design theme
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, methodology and data. Strong interpersonal, communication and organisational skills. Please note, that DBS check is required as part of the research and will form a condition of the offer. Desirable: Experience
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, Mathematical Sciences, Materials Science and Engineering, and Chemical Engineering are welcome to apply. If you are seeking an exciting opportunity to become part of a multidisciplinary team of esteemed
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Project Description: This funded Humanities PhD project will be conducted as part of the new Centre for Net Positive Health and Climate Solutions. The project will focus on cultural histories of eco
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. Yutaka Sumino, Tokyo, Japan), gaining exposure to both theoretical and experimental aspects of the field. Opportunities for lab visits abroad will further enrich your research experience. You will be part
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evaluation. Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle
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of thousands of potential materials. A key component of the project is the creation of a high-quality defect database for functional mixed-anion semiconductors, which will be used to train the machine learning
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discrete (cyber) and continuous (physical) components. The core technical innovation lies in modelling the verification problem as a delta-decision problem, solved using an extended SMT (Satisfiability