An exciting opportunity has arisen for a postdoctoral fellow (PDF) position within the Risk-Informed Lifecycle Infrastructure Engineering (RILCIE) research group led by Prof. Arnold Yuan at the Department of Civil Engineering, Ryerson University.
We are looking for an enthusiastic, highly motivated researcher to work on a project relating to collaborative infrastructure asset management using deep reinforcement learning and assisted learning. The candidate is expected to have excellent stochastic deterioration modelling and machine learning skills and research experience related to infrastructure asset management. The PDF will have opportunities to work in an expanding multidisciplinary research group at the downtown core of the City of Toronto, Ontario, Canada.
Qualifications:
- At least one of the degrees (Bachelor’s, Master’s, and Doctoral degrees) was studied in civil engineering;
- At least two years analyzing infrastructure deterioration data;
- Proficiency in MATLAB or Python;
- Working knowledge in ArcGIS;
- Experience relating to climate change effects on infrastructure systems will be a good asset;
- Detailed understanding of infrastructure asset management, and must be very familiar with the planning, design, construction, maintenance, condition assessment and performance evaluation, and rehabilitation of at least one of the following asset classes: bridges, pavements, and sewers.
The position will start in June 2022; earlier start is possible. This is a one-year appointment and renewable for two years, depending on budget and their performance.
To Apply:
Please email a detailed CV (including past experience, publications, poster/oral presentations and any other significant achievements) and the contact information for two referees to the contact information below:
Dr. Arnold (X.-X.) Yuan, Professor, P. Eng.
Chair, Department of Civil Engineering
Ryerson University (Renaming in process)
Email: arnold.yuan@ryerson.ca
Website: rilcie.blog.ryerson.ca