59 data-"https:"-"https:"-"https:"-"https:"-"Bath-Spa-University" positions at Cranfield University
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capable of dynamically adjusting their collaboration strategy—such as autonomy level, motion behaviours, and information transparency—based on real-time human trust. By aligning vehicle behaviour with
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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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, or question, An explanation of the proposed original contribution to knowledge, A brief review of relevant literature, A summary of intended research methodology and data collection approach, A statement on the
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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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should have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge
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in research, data analysis and stakeholder engagement. You will demonstrate initiative and the ability to work both independently and as part of multidisciplinary and international teams. Experience
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reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate prognostic models for filter
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mitigating jamming and spoofing threats in real-time. Integration of Trusted Execution Environments (TEEs): Investigate the use of TEEs to create secure zones within embedded systems, facilitating secure data
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identification of cracking risk, even where experimental data are limited. Project Overview This EngD will develop a thermodynamic modelling framework to predict the formation of damaging liquid phases in turbine
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establishing events and aiding future design improvements. Currently, there remains a paucity of data in this domain, making it difficult to identify any notable trends and associated failure mechanisms