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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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as an academic member of the MK:U Education team with a specialism in a field related to Data or Digital (for example, analytics, visualisation, maths and programming, Big Data). This element of
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to travel to field sites overseas. You will have experience of working with farmers and other rural stakeholders, ideally using participatory methods and collection of field data (e.g. environmental
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support appraisal of scientific experimentation with numerical and statistical analysis, and work autonomously to analyse, interpret, and evaluate scientific data and present the results of laboratory work
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facilities to ensure timely testing of energetics • Run analyses and tests using specified and agreed techniques and models • Acquire and interpret research data and results
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economy and de-centralised manufacturing. To enhance process efficiency (clean) by researching sensors and big data for factory management, product service systems, factory modelling and artificial
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utilised with the aim of efficiently analysing large datasets, the extraction of meaningful information and the creation of a chemical database. The PhD student will be involved in the adaptation of the CRIM
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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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postgraduate university, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and