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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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these sectors, design customized technological solutions using advanced sensing and analytics, and validate these innovations through real-world implementation. The project will bridge the gap between theoretical
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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing
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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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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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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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, deposition and characterisation tools, including bespoke burner-rig testing design for realistic thermal testing. Combination of experimental, analytical, and modelling training, ideal for interdisciplinary
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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