|Location:||Cork - Ireland|
|Funding for:||UK Students, EU Students|
|Funding amount:||€18,500 to €39,500 or £15,947.50 to £34,050.07 (converted salary*) - please see advert|
|Placed On:||12th May 2021|
|Closes:||12th August 2021|
Supervision team: Dr. Zili Li (UCC) and John Osborne (the European Centre for Nuclear research (CERN))
This 4-year PhD project will conduct an innovative field monitoring and analysis of ageing tunnel infrastructure at the European Centre for Nuclear research (CERN) over tens of miles. Unlike conventional manual inspection at high cost of labour and time, two types of innovative field technologies will be developed and improved in this project: 1) Distributed Fibre Optic Sensing (DFOS) and associated fibre optic data processing code; 2) Automated defect classification software for large-scale tunnel infrastructure using an autonomous camera system. The advanced monitoring technologies will give more insights into ageing CERN tunnel behaviour both globally over tens of miles and specially at local critical deteriorating sections. The gathered field data will be analysed together with hydrogeological environment and structural features along the monitored tunnel route for more comprehensive structural health assessment than previously available by conventional methods, providing insightful guidance for underground infrastructure maintenance and design in CERN, Ireland and worldwide.
Position Description: This 4-year PhD full-time position is jointly funded by Irish Centre for Research in Applied Geosciences (iCRAG) and CERN. The successful candidate will be registered at University College Cork, starting in September 2021 or soon thereafter. The stipend is €18,500 per annum at UCC in Year 1 & 4, while ca. €39,500 per annum at CERN in Year 2 & 3, with a contribution to tuition fees of €5,500 per annum.
Candidate Experience: The candidate should have a minimum 2.1 in an honours Bachelor’s degree in Civil Engineering, or an equivalent standard from an overseas university. All applicants whose first language is not English must provide evidence of English language proficiency (e.g. IELTS or TOEFL) according to UCC entry requirement. Some experience in fibre optic sensing, structural health monitoring and machine learning for civil/geotechnical/geological engineering would be an advantage.
Application: Please e-mail a CV (max. 2 pages) and a cover letter outlining your experience and motivation to Dr. Zili Li (firstname.lastname@example.org).
Type / Role: