PhD Studentship: SHM-based life-cycle asset management of civil infrastructure
University of Surrey
|Funding for:||UK Students, EU Students|
|Placed on:||6th September 2016|
|Closes:||31st October 2016|
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Supervisors: Dr Ying Wang
Prof. Marios Chryssanthopoulos
Funding status: Directly Funded Project (European/UK Students only)
Deadline: Monday 31st October 2016
Project title: SHM-based life-cycle asset management of civil infrastructure
The safety and integrity of civil infrastructure is a critical concern in our society and a challenge for our research community. To address such issues, next-generation materials and structures have been envisioned as being engineered with smart features and abilities to monitor their own condition through a comprehensive sensor network in either passive or active ways. These sensors should be able to evaluate structural integrity indicators and provide maintenance/management recommendations. In this respect, the huge quantities of structural health monitoring (SHM) data that can be acquired offer not only opportunities to help engineers improve the safety and maintainability of critical structures, but also introduce new challenges which require further advances in fundamental research and applied technologies. At Surrey, we have so far investigated a range of issues related to the application of SHM techniques in metallic bridges, pipelines and other structures, bringing together expertise in materials and structures, as well as statistics, informatics and decision theory.
Well qualified and strongly motivated PhD candidates are invited to join an expanding research group working under the supervision of Dr Ying Wang on SHM-based life-cycle asset management. Applicants will be selected using the following criteria:
- Applicants should have (or expect to obtain by the start date) at least an Upper Second Bachelor’s degree, and preferably a Master’s degree, in an Engineering subject.
- Applicants are expected to have excellent analytical skills and a solid background in structural mechanics/dynamics and advanced numerical modelling of structures (e.g., finite element modelling).
- Applicants with a deep understanding of statistical inference and/or machine learning techniques will be preferred.
- Relevant professional experience is welcomed.
To apply please send your CV and a covering letter detailing your experience and why you’re right for this role to: email@example.com
For application enquires please contact firstname.lastname@example.org
For more information about our Department please see: http://www.surrey.ac.uk/cee/
Funding is available for UK / EU nationals only and covers full tuition fees (home rate) and a stipend at the rate specified by the Research Council (rate for 2016-2017 is £14,057 p.a. tax-free). The award will be for a period of 3 years from a mutually agreed starting date (one of: 1st October, 1st January, 1st April, 1st July).
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