39 data-"https:"-"https:"-"https:"-"CEA-Saclay" PhD positions at Cranfield University
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performance degradations and unwarranted system failures can occur. There is certain physical information known a priori in such aerospace platform operations. The main research hypothesis to be tested in
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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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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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usability and accuracy, as well as conducting field tests to validate their effectiveness. Additionally, the research will explore the economic viability of these sensors to enhance real-time data collection
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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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training will be provided. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods. There is flexibility to tailor the research to your
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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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