72 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"FCiências" positions at Cranfield University
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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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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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significantly to the current body of knowledge. This experience will equip you with valuable research skills, including methodologies, data analysis, and critical thinking, highly sought after in both academic
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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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for more information. •30 September 2024 •27 January 2025 •2 June 2025 •29 September 2025 We highly recommend you prepare the following information, as this will be requested at the application stage. Your
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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