The School of Civil and Environmental Engineering (CEE) at Nanyang Technological University (NTU Singapore) is a leading school for research and education in the sustainable built environment. The Risk and Infrastructure Systems Lab, led by Asst. Prof. Alex Sixie Cao, develops quantitative methods for understanding and managing the reliability, robustness, and safety of structures and infrastructure systems under uncertainty.

We are seeking a Research Fellow to develop advanced computational methods for the reliability and robustness assessment of structural and infrastructure systems. The role will focus on efficient probabilistic analysis of high-dimensional and dynamic systems, including advanced sampling, surrogate modelling, and AI or machine-learning methods where appropriate.

Key Responsibilities:

• Develop advanced computational methods for reliability and robustness assessment of structural and infrastructure systems under uncertainty.

• Develop and implement efficient probabilistic methods for high-dimensional, time-dependent, and dynamic problems, including advanced sampling and surrogate modelling techniques.

• Investigate the use of AI and machine-learning methods to improve the efficiency and scalability of reliability, rare-event, and system-level analyses.

• Apply the developed methodologies to structural and infrastructure-system case studies, including problems involving damage propagation and system robustness.

• Develop, maintain, and document research codes and computational workflows supporting the project.

• Prepare research findings for publication in high-quality international journals and presentation at scientific conferences.

• Contribute to the preparation of research proposals and grant applications.

• Collaborate with members of Lab and external research partners and contribute to related research activities within the lab.

• Supervision of students in relevant topics.

Job Requirements:

• PhD in Civil Engineering, Structural Engineering, Engineering Mechanics, Applied Mathematics, or a closely related field.

• Strong background in structural reliability, probabilistic modelling, stochastic dynamics, uncertainty quantification, or related areas.

• Experience in developing and implementing computational methods for engineering analysis.

• Proficiency in scientific programming, preferably using Python, MATLAB, C/C++, or comparable programming languages.

• Experience with advanced sampling methods, surrogate modelling, machine learning, or AI-assisted computational methods is desirable.

• Strong publication record, or demonstrated potential to publish good articles, in relevant peer-reviewed international journals.

• Good written and oral communication skills.

• Ability to conduct research independently while working effectively within a collaborative research environment.

• Strong analytical, problem-solving, and scientific writing skills.

• Interest in contributing to research proposal and grant development.

• Entry-level candidates who have recently completed their PhD are welcome to apply.

We regret to inform that only shortlisted candidates will be notified.