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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big
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Self-funded PhD opportunity in 6G as part of major project hub, further funding possible subject to progress of project and student. Focus on native AI in 6G systems with experimental testbed and
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University. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power Engineering, Cranfield University, in the area of gas turbine performance, diagnostics and prognostics
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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advanced simulation methods, including Reynolds-Averaged Navier-Stokes (RANS), Direct Numerical Simulations (DNS), and/or Large Eddy Simulations (LES), will be employed to accurately model the complex flow
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This fully funded PhD, with a stipend of £20,000 pa, is sponsored by the Royal Horticultural Society and EPSRC. The research offers an exciting opportunity to develop a nature-based solution (NbS
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specific courses aimed at the different phases of a PhD. For example, the programme includes aspects such as research methods, technical report writing, presentation skills, data management, leadership
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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This PhD project will investigate the recent field of study of Causal Machine Learning, which aims to modify and augment Machine Learning by using Causal Analysis techniques as a way to solve its