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Big data analytics for industrial application
PhD
In Bedfordshire ()
Description
-
Type
PhD
Big Data analytics has attracted intense interest from both academia and industry recently for its attempt to extract more useful information and knowledge from Big Data. Big Data analytics will help to develop more advance diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine leaning exists as the most promising technologies of big data analytics in industrial problems. The student will have the opportunity to work with experts in the data analytics 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 exclusively postgraduate university that is a global leader for transformational research and education in technology and management. Research Excellence Framework 2014 (REF) has recognised 81% of Cranfield’s research as world leading or internationally excellent in its quality. Every year Cranfield graduates the highest number of postgraduates in engineering and technology in the UK (Source: Higher Education Statistics Agency Ltd). Cranfield Manufacturing is one of eight major themes at Cranfield University. The manufacturing capability is world leading and combines a multi-disciplinary approach that integrates design, technology and management expertise. We link fundamental materials research with manufacturing to develop novel technologies and improve the science base of manufacturing research. The Integrated Vehicle Health Management (IVHM) Centre is a major collaborative venture at Cranfield, started in 2008, with funding from the East of England Development Agency (EEDA); a consortium of core industrial partners, (Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD and Alstom); and from EPSRC. The investment, over the first 5 years of operation, was approaching £10M. We are now in our eighth year of operation and the Centre has...
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Subjects
- University
- Technology
Course programme
Entry requirements
- A minimum of a 2:1 first degree in a relevant discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum 60% mark in the Project element or equivalent with a minimum 60% overall module average.
- the potential to engage in innovative research and to complete the PhD within a three-year period of study.
- a minimum of English language proficiency (IELTS overall minimum score of 6.5).
Also, the candidate is expected to:
- Have excellent analytical, reporting and communication skills
- Be self-motivated, independent and team player
- Be genuine enthusiasm for the subject and technology
- Have the willing to publish research findings in international journals
Big data analytics for industrial application