Research Fellow (Civil/Structural Engineering, Engineering Mechanics or Applied Mathematics)
Core
Develop advanced computational methods for reliability and robustness assessment of structural and infrastructure systems under uncertainty.
Role type
Research Fellow (Civil/Structural Engineering, Engineering Mechanics, or Applied Mathematics)
Builds
Research codes, computational workflows, and scientific publications
Domain
Civil and Environmental Engineering, Structural Reliability, Uncertainty Quantification
Deliverable
research
Required skills
probabilistic modelling, stochastic dynamics, uncertainty quantification, scientific programming (Python, MATLAB, C/C++), advanced sampling methods, surrogate modelling
Preferred skills
AI and machine-learning methods, rare-event analysis, system-level analysis, grant proposal development
Technologies
Python, MATLAB, C/C++
Responsibilities
Develop and implement efficient probabilistic methods for high-dimensional, time-dependent, and dynamic problems; Investigate the use of AI and machine-learning methods to improve efficiency and scalability; Apply methodologies to structural and infrastructure-system case studies; Develop, maintain, and document research codes and computational workflows; 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; Supervision of students in relevant topics
Seniority
Entry-level (recent PhD graduates welcome)