12 modal-time-freqeuncy-artificial-intelligence PhD positions at University of Sheffield
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Exciting Fully Funded PhD Opportunity: Novel Sealing for High-Pressure H2 and Low-Carbon Storage Technologies - Help Shape the Future of Clean Energy Storage! School of Mechanical, Aerospace and
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, curious individual to join an exciting PhD. This opportunity is generously funded by John Crane Ltd, a world-renowned engineering technology leader. Why This PhD? Impact Clean Energy's Future: Develop next
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). Real-World Impact: Contribute to transformative technologies in clean energy and carbon capture. Future job opportunities: Digital modelling and computational fluid dynamics are highly sought after
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Exciting Fully Funded PhD: Unlock the Future of Turbo Gas Seal Technology with John Crane Ltd! School of Mechanical, Aerospace and Civil Engineering PhD Research Project Competition Funded UK
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Santos, Dr RG Bryant, Dr Chris Bousfield Application Deadline: 31 October 2025 Details We are seeking a motivated PhD candidate to develop innovative artificial intelligence methods for plant species
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of these assessments can be influenced by factors such as the call handler’s expertise, call volumes, and stress levels, potentially delaying life-saving interventions. Emerging advancements in artificial intelligence
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Exciting Fully Funded PhD: Computational Modelling for High-Pressure, Low-Carbon Storage Technologies. Be a Key Player in Shaping the Future of Clean Energy Storage! School of Mechanical, Aerospace
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Exciting Fully Funded PhD Opportunity: Drive Innovation in Hydrogen Technology. Project: High-Pressure Hydrogen Generation, Storage, and Use - Shape the Future of Clean Energy with Us! School of
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of Robot Behaviours in Simulation: Develop methods to automatically generate diverse test scenarios in a virtual environment to efficiently find faults in robotic skills. This involves using intelligent
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, but current methods are not always efficient or optimal. The process lacks an intelligent, informed approach to selecting the best grinding parameters, which can lead to inefficient maintenance actions