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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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eager to adopt practical solutions. The student will also benefit from the Cranfield Environment Analytical Facility providing access to state of the art chemical and genomics equipment. The findings will
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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existing data analytics tools will help deploy these technologies in the industry context without the need for big datasets. Predictive Maintenance (PdM) is one of the maintenance strategies that has
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-Based Modelling ✔ Data Science & Advanced Analytics with real-world SCADA data ✔ Renewable Energy Systems & Wind Turbine Engineering ✔ Industry Engagement & Research Commercialization ✔ Scientific Writing
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Engineering, Environmental Chemistry, Environmental Science, or Chemical Engineering. It is essential that the candidate has experience and/or an understanding of analytical chemistry and biological wastewater
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architectures will sharpen their ability to design trustworthy electronics at scale. In parallel, students will strengthen analytical reasoning, ethical decision-making, secure systems thinking, and research
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combination of skills in intelligent system validation, safety assurance, and lifecycle analytics. They will also develop transferable abilities in technical reporting, stakeholder communication
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. Develop analytical and finite element (FE) models to investigate the extent and sources of nonlinear behaviour in LGSs. 3. Develop novel control strategies to stabilise LGS shape, orbit & attitude
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-Based Modelling ✔ Data Science & Advanced Analytics with real-world SCADA data ✔ Renewable Energy Systems & Wind Turbine Engineering ✔ Industry Engagement & Research Commercialization ✔ Scientific Writing