37 parallel-computing-numerical-methods-"Prof"-"Eindhoven-University-of-Technology-(TU" PhD positions at Cranfield University in United Kingdom
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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validation experiments for modelling • Computational fluid dynamics techniques • Finite element analysis method • Reviewing literature, planning and managing research, writing technical report / paper
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Resilience (WIRe) , a prestigious collaboration between Cranfield University, the University of Sheffield, and Newcastle University. The WIRe programme offers bespoke training that hones both technical and
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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information from unintended data leakage. Communication Resilience Against Jamming and Spoofing: Develop AI-driven methods to detect and mitigate jamming and spoofing attacks, enhancing the robustness
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pressure to reduce both energy demand and chemical consumption. Project SandSCAPE, an Ofwat-funded programme, tackles this challenge by integrating purpose-built robots that skim slow sand filter beds while
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for Security Operations Centres (SOCs) while pioneering strategies for quantum-era resilience. This project sits at the intersection of Artificial Intelligence, Cybersecurity, and Explainable Computing. It
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and nanomaterials at the Composites and Advanced Materials Centre (Dr Sameer Rahatekar, Prof Krzysztof Koziol) and Hyper-velocity impact testing facilities at Centre for Defence Engineering and Physical
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targets the development of advanced coatings to prevent cell-to-cell propagation during runaway events. It combines experimental studies, numerical modelling, and real-world burner rig testing, culminating
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critical to ensuring the longevity and safety of fusion reactors. This PhD project focuses on developing an integrated framework that combines cutting-edge computational models, including Monte Carlo