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systems that continuously assess the health of components, predicting failures before they occur. Compliance Assurance Techniques: Design AI-driven methods to ensure ongoing compliance with industry
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(Applications) through reliable quantum advantage assessment. Project Description The project addresses the critical need for reliable, scalable verification and benchmarking schemes in quantum computing. Current
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energy data lifecycle spans pre-construction (e.g., meteorological mast data, LiDAR data, wind climate and energy yield modelling, environmental impact assessment data), operational phases (e.g., SCADA
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paid. We expect the stipend to increase each year. UK students and those with settled status are eligible to apply. The outcomes of this research are expected to contribute to both technological
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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. This transition is driven by the agri-tech sector, providing affordable and quick assessment tools that empower farmers with soil health data. This project aims to understand farmers' responses to these changes and
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demonstrate the utility of an adaptive mesh refinement approach in interface resolving Computational Fluid Dynamics (CFD) simulations of flow boiling at conditions relevant to nuclear thermal hydraulics
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prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining useful life of electronic components, supporting studies in electronic system
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and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining
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“Assessing and controlling seizure-modulating fluctuations (ACORN)”, we are excited to recruit a motivated individual with research experience in a relevant subject area to work with us on this exciting