35 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Bournemouth University" scholarships at Cranfield University
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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for this research studentship, please complete the online application form. For further information contact us today: T: +44 (0)1234 758540 E: study@cranfield.ac.uk
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for the collection of data to develop and validate prognostic models for filter degradation. Integrated Drive Generator (IDG) Rig: Simulates the operation of an aircraft's IDG, used to investigate fault detection
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researchers with access to data, equipment or participants for a study, additional funding for their studentship, an additional supervisor, and/or a placement. What is SENSS looking for? Interesting research
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to enhance real-time data collection and improve monitoring practices, and the social factors that influence their uptake. This studentship offers a unique opportunity to engage in groundbreaking research with
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opportunities. How to apply If you are eligible to apply for this research studentship please complete the online application form . For further information please contact Dr. Sameer S Rahatekar E: S.S.Rahatekar
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influence perceptions. It will subsequently explore and evaluate the types of information and knowledge required to improve the understanding and appreciation of urban blue spaces, before investigating
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responses and mitigation scenarios across realistic European agricultural landscapes (illustrative example shown in Figure 1). There is also an opportunity to collect original movement and behavioural data
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, environmental science, urban sustainability, geospatial analysis, or quantitative modelling. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods
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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at