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350983844鐵蟲(chóng) (初入文壇)
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[交流]
PhD vacancy in Civil / Digital Engineering at UCC & CERN
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Project title: Large Language Model and AI for ground-tunnel interaction and infrastructure maintenance Supervision team: Dr. Zili Li (UCC, Ireland) and CERN (the European Centre for Nuclear research, Geneva, Switzerland) Project description: Large-scale underground infrastructure, such as tunnels and pipelines, often experiences significant performance degradation over long-term service. To assess structural health and long-term safety, various data sources—including crack images, ground-penetrating radar (GPR) images, continuous strain sensor data, and historical borehole logs—are utilized. However, evaluations are typically conducted on individual datasets rather than through a comprehensive integration of multiple data sources. This study addresses this limitation by proposing a systematic framework that integrates diverse data to develop a Large Language Model and AI for underground infrastructure. The model aims to establish underlying correlations between different data sources and the condition of concrete linings, offering valuable insights into the long-term behavior of large-scale underground structures under varying hydrogeological conditions. Candidate Experience: The PhD candidate should hold at least a 2.1 honours Bachelor’s degree in Civil Engineering, Computer Science, or an equivalent qualification from an overseas institution. Currently, a 50% PhD scholarship is available, with potential for additional funding contingent upon satisfactory academic progress. Candidates whose first language is not English must provide proof of English language proficiency (e.g., IELTS or TOEFL). Prior experience in structural health monitoring and machine learning is advantageous. Application: • PhD student applicant Please e-mail a CV (max. 2 pages) and a cover letter outlining your experience and motivation to Dr. Zili Li (zili.li@ucc.ie or zili.li@mit.edu). Relevant paper for your reference: Image‐based large language model approach to road pavement monitoring S Xu, K Zhao, J Loney, Z Li, A Visentin - Computer‐Aided Civil and Infrastructure Engineering, September 2025, https://doi.org/10.1111/mice.70075 |
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