47 condition-monitoring-machine-learning-"Multiple" PhD positions at Cranfield University
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Technology. Mr Kumar is the module leader for Military Vehicle Dynamics, part of the Military Vehicle Technology MSc, which he teaches in the UK and overseas. He worked on project from the UK Ministry of
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The project includes a detailed literature survey leads to the development of a test matrix, includes test conditions, type of alloys used including bare or coated alloys. The project includes testing
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experts in the prognostics and condition monitoring field, as well as being part of our strong and dynamic research centre at Cranfield University. About the host University/Centre Cranfield is an
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within fusion reactors, especially plasma-facing materials (PFMs) exposed to intense heat fluxes and energetic particles. Understanding and predicting how these materials degrade under such conditions is
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
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, these systems serve as complex functional approximators trained over an input-output data set. ‘Second Wave AI’ is the term used to describe the current glut of 'machine learning' style intelligence, where
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instrumentation for acoustic flow measurements, sensitivity to intake operating conditions and the exploration of data analysis methods to improve the overall measurement system accuracy. It will also include
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apparatus equipped with thermocouples and thermal imaging to simulate realistic runaway events. Top-performing coatings will be validated in situ on live EV cells under controlled runaway conditions. Dr
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by priority chemicals in surface water. There remains a critical need to evaluate the efficacy of NbS in mitigating these statutory micropollutants. Following the monitoring, it is also essential
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and design criteria to prevent hydrogen-induced failure in metallic aerospace materials. Through experimental testing of ferritic and austenitic steels under realistic service conditions, including