The University of Strathclyde’s Advanced Nuclear Research Centre (ANRC) is offering a 3 year fully funded PhD in the field of industrial informatics, to investigate the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to Prognostics and Health Management (PHM) in the nuclear industry. The ANRC conducts industrially relevant research in collaboration with extensive domestic and international industry partners; hence, the successful candidate should expect extensive industrial engagement with the intention to deploy the resulting research within industry.
The application of AI and ML techniques to the PHM of nuclear assets presents issues in terms of regulatory compliance (i.e. transparency of decision making) and the lack of case studies to build representative diagnostics and prognostic models (i.e. components rarely, if ever, fail). The successful candidate will investigate these (and similar) issues through a number of different approaches, including: - Active learning for the detection of asset degradation advancement through effective utilisation of engineering expertise in order to update underlying models efficiently; - Incremental learning where underlying data representations can evolve to reflect the most recent information available; - Transfer learning to solve the challenges of limited data-label availability and rapid domain adaption; - Intelligent condition monitoring architectures for the deployment of advanced analytic techniques within the nuclear industry.
The ultimate objective of the PhD is to support the nuclear industry maintain its asset base, through the full asset life cycle, by developing better (and trustworthy) tools that extract (and supplement) reliable information from plant condition monitoring data. The research will focus on the creation of advances in analytical techniques and human-centric AI as relates to their industrial application. This permits nuclear operators to confidently interact with the abundance of data available from legacy and future systems, and ensure the most effective utilisation of expert demographics within their organisations.
PhD Tasks and Responsibilities: • Reviewing the state-of-the-art in AI and ML as applied in the nuclear industry in order to subsequently scope the PhD and associated research tasks; • Fundamental research to support the development of related technologies; • Domestic and international conference attendance and presentation; • Peer-reviewed academic journal publications.
Applicant Qualifications and Preferences: • High quality undergraduate or Master degree in a relevant field (i.e. engineering, mathematical or scientific); • Relevant experience in Artificial Intelligence techniques (i.e. data- and knowledge-based systems); • Proactive initiative with the ability to work both independently and as part of a team; • Adhere to strict standards and confidentiality; • Excellent organisational and communication skills; • Excellent written and spoken English.
The student would join the University of Strathclyde’s 60-credit postgraduate training programme leading to the Postgraduate Certificate in Researcher Professional Development.
The student will also benefit from interaction with other academics and PhD students within the ANRC’s active research programme, as well as being embedded within the EPSRC Prosperity Partnership Programme that includes multiple industrial partners and additional academic institutions.
The PhD student will gain a range of technical, practical and problem solving skills required by the machine learning and nuclear industries. With the specific technical expertise a wide range of career routes is possible including power industries, consultancy and government agencies. Further academic and research routes will also be possible.
Planned start date for the PhD is in October 2020. However, with respect to the current (April 2020) status of working conditions at the University of Strathclyde, if these conditions are required to be extended, the candidate would be welcomed to start on a remote working basis.
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