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. References: you will need to provide details of two referees as part of your application. Closing date The closing date for applications is 01 July 2025. Further Information It is expected that interviews will
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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 access to community dietetic
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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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, 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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, 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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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