Rocio Lilen Segura
1 month ago
Fully Funded PhD in Structural Engineering, Reliability and Risk Analysis – Polytechnique Montréal Polytechnique Montréal in Canada
Degree Level
PhD
Field of study
Risk Assessment
Funding
This is a fully funded PhD position. The funding covers tuition and provides a stipend, though the exact amount is not specified.
Deadline
Expired
Country
Canada
University
Polytechnique Montréal

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Where to contact
Official Email
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About this position
A fully funded PhD opportunity is available in the Group for Research in Structural Engineering (GRS) at the Department of Civil, Geological, and Mining Engineering, Polytechnique Montréal, starting Fall 2026. The research will focus on reliability-based and performance-based assessment of critical infrastructure, particularly dams and water-retaining structures. The project aims to advance beyond traditional deterministic safety factors by developing probabilistic, data-driven, and decision-oriented frameworks that better account for uncertainty, failure modes, and real-world operating conditions.
Key research areas include reliability and risk analysis, computational and numerical modeling, machine learning and AI for engineering applications, and probabilistic methods and uncertainty quantification. The position is ideal for candidates with a background in structural engineering and a strong interest in these topics. The research group is led by Assistant Professor Rocio Lilen Segura, and the work will be conducted in Montréal, Canada.
The position is fully funded, covering tuition and providing a stipend. Applicants should submit a CV, transcripts, a 1-page description of their background and research interests, and the names and contact information of three referees. Strong MSc students or early-career engineers interested in advanced research and industry-relevant impact are encouraged to apply. The application deadline is December 28, 2025.
For more information or to apply, contact Rocio Lilen Segura at [email protected]. Please share this opportunity with interested candidates.
Funding details
This is a fully funded PhD position. The funding covers tuition and provides a stipend, though the exact amount is not specified.
What's required
Applicants should have a background in structural engineering and a strong interest in reliability and risk analysis, computational and numerical modeling, machine learning or AI for engineering applications, and probabilistic methods and uncertainty quantification. Candidates should submit a CV, transcripts, a 1-page description of their background and research interests, and names and contact information for three referees. Strong MSc students or early-career engineers are encouraged to apply.
How to apply
Email Rocio Lilen Segura with your CV, transcripts, a 1-page description of your background and research interests, and the names and contact information of three referees.
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