Faculty of Engineering
Research project description
Our research team is developing a Generation Risk Assessment (GRA) models to optimize the performance of power generation systems. As a part of this project, we are developing a system reliability coupled with the Bayesian updating framework in order to facilitate dynamic updating of risk and reliability estimates as new inspection and surveillance data become available. There is an opening for a fully-funded PhD student in this research project.
Fields of research
- Reliability analysis
- Bayesian probability
- System reliability analysis
- Computational methods
- Modern programming tools and methods
- Industrial engineering systems
Qualifications and ideal student profile
Prospective graduate student researchers must meet or exceed the minimum admission requirements for the programs connected to this opportunity. Visit the program pages using the links on this page to learn more about minimum admission requirements. In addition to minimum requirements, the research supervisor is looking for the following qualifications and student profile.
- Strong background in programming, computational methods, probability theory and system analysis
- An ideal student should have a degree in System Engineering, Computer Science, or Data Science
- An applicant with a strong background in Structural Reliability Theory will also be desirable
Faculty researcher and supervisor
- Mahesh Pandey
Professor, Civil and Environmental Engineering
View faculty profile →
NSERC-UNENE Chair in risk-based life cycle management of engineering systems lab website →
Graduate programs connected to this project
Important dates
Bayesian System Reliability Analysis of Engineering Systems is accepting applications for the winter 2027 term.
If you are interested in the fall 2026 intake, please contact the faculty researcher and supervisor.