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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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insight, and who are used to working across government, military, and industry settings. Research Students Find out more about our current research students and their areas of research . Mode of study Mode
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image correlation is an effective tool to characterize material properties. The analysis of the images can provide a fair assessment about the changes in material behaviour under different operational
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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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. Although there is a clear synergy between fatigue damage and corrosion, most fatigue prognosis models do not explicitly consider the role of the environment, which is usually reduced to obscured fitting
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of £60,099 to £69,505 per annum, with potential progression up to £85,000per annum. About the Role The Faculty of Engineering and Applied Sciences is seeking to appoint within the Centre of Assured and
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be able to help you develop this. This vacancy may be filled before the closing date so early application is strongly encouraged. For further information about application please visit Applying for a
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wealth of social and networking opportunities. Please contact us to learn about the specifics in your chosen area of research. Research areas Power and Propulsion Engineering The Centre for Propulsion and
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mechanism. The integrating should enable to guarantee certain properties of the learned functions, while keep leveraging the strength of the data-driven modelling. Most of, if not all, the traditional
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the most complex and interesting technological areas such as the aerospace and transport field. Cranfield University works with over 1,500 organisations, including the leading global aerospace and automotive