Fully Funded PhD in Civil Engineering (Structural Health Monitoring, Data-driven Bridge Assessment)

University College Dublin (UCD) is offering a fully funded PhD scholarship in Civil Engineering, focusing on structural health monitoring and data-driven bridge assessment. The position is based in the Structural Dynamics and Assessment Laboratory (SDA-Lab), led by Prof. Eugene OBrien and Dr. Abdollah Malekjafarian. The research project, NETSENS, is funded by Research Ireland and aims to develop unsupervised data-driven frameworks for drive-by bridge monitoring, leveraging machine learning and advanced data analysis techniques to assess bridge conditions without direct installation of sensors. The successful candidate will work on developing feature engineering for unsupervised learning using real-life experimental data, focusing on condition-sensitive features from drive-by measurements. The project includes the development of novel detection algorithms and validation using lab-scale and full-scale bridge experiments. Collaborators include experts from the University of Sheffield and Queen’s University Belfast. Applicants should hold an honours Level 8 degree in science or engineering (Civil, Structural, Mechanical, or related discipline), with strong communication, writing, and time management skills. Desirable qualifications include a master’s degree, research experience, proficiency in programming (Matlab, Python), and knowledge of machine learning, data analysis, and structural dynamics. The ability to work both independently and collaboratively is essential. The scholarship covers a stipend of €25,000 per annum, travel/consumables/materials budget, and EU tuition fees for four years (non-EU fees in exceptional cases). Applications must be submitted online via the provided form by January 30, 2026. For further details, see the official UCD admissions page and contact Dr. Abdollah Malekjafarian for queries. Keywords: Civil Engineering, Structural Health Monitoring, Structural Dynamics, Bridge Monitoring, Data-driven Methods, Machine Learning, Anomaly Detection, Engineering.