Research Fellow – Reliability & Robustness of Structures

About the role

The School of Civil and Environmental Engineering at Nanyang Technological University (NTU) seeks a Research Fellow to develop advanced computational methods for reliability and robustness assessment of structural and infrastructure systems. The role focuses on efficient probabilistic analysis of high‑dimensional and dynamic systems using advanced sampling, surrogate modelling, and AI techniques.

Key responsibilities

• Develop advanced computational methods for reliability and robustness assessment of structural and infrastructure systems under uncertainty.
• Implement efficient probabilistic methods for high‑dimensional, time‑dependent, and dynamic problems, including advanced sampling and surrogate modelling techniques.
• Investigate AI and machine‑learning approaches to improve efficiency and scalability of reliability, rare‑event, and system‑level analyses.
• Apply methodologies to case studies involving damage propagation and system robustness.
• Develop, maintain, and document research codes and computational workflows.
• Prepare research findings for publication in high‑quality international journals and present at scientific conferences.
• Contribute to research proposals, grant applications, and collaborate with lab members and external partners.
• Supervise students on relevant topics.

Required profile

• PhD in Civil Engineering, Structural Engineering, Engineering Mechanics, Applied Mathematics, or a closely related field.
• Strong background in structural reliability, probabilistic modelling, stochastic dynamics, or uncertainty quantification.
• Experience developing and implementing computational methods for engineering analysis.
• Good written and oral communication skills and ability to work independently and collaboratively.
• Strong analytical, problem‑solving, and scientific writing abilities.
• Interest in contributing to research proposals and grant development.

Required skills

• Scientific programming proficiency, preferably Python.
• MATLAB.
• C/C++.