The Chair of Risk, Safety & Uncertainty Quantification (RSUQ) at ETH Zurich develops cutting-edge methodologies in the field of uncertainty quantification (UQ) for engineering systems. Our research covers surrogate modeling, reliability analysis, sensitivity analysis, optimization under uncertainty, and Bayesian calibration. We are known for developing the UQLab software framework for uncertainty quantification, which is widely used in academia and industry.

This PhD position is part of the ORACLES project ("Optimization, Reliability And CaLibration using Emulators of Stochastic computational models"), funded by the Swiss National Science Foundation (SNSF). The project aims to significantly advance the state-of-the-art in uncertainty quantification (UQ) by developing and applying novel stochastic emulators. A key focus is using these advanced emulators to tackle complex, UQ problems previously intractable with deterministic simulators, such as high-dimensional reliability-based design optimization and hierarchical Bayesian inversion. This specific PhD position focuses on the challenges within hierarchical Bayesian inference.

As the successful candidate, your research will focus on developing and applying advanced computational methods for Bayesian inference and model calibration. Your main tasks will include:

• Investigating and developing novel computational strategies to enhance the efficiency and scalability of Bayesian methods for complex models
• Exploring the use of advanced surrogate modeling techniques (e.g., stochastic emulators) to accelerate likelihood computations and posterior exploration in Bayesian frameworks
• Addressing challenges related to high-dimensional parameter spaces and complex, potentially high-dimensional model outputs in Bayesian analysis
• Developing methods to improve the accuracy and robustness of parameter estimation and uncertainty quantification using Bayesian techniques
• Applying the developed methods to calibrate and validate computational models against experimental data in relevant engineering contexts
• Disseminating research findings through publications in leading peer-reviewed journals and presentations at international conferences

We are looking for a highly motivated candidate with:

• A Master's degree in Computational Science/Engineering, Applied Mathematics, Mechanical Engineering, Civil Engineering, or a related field
• A strong background and keen interest in uncertainty quantification, structural reliability, optimization, Bayesian inference, surrogate modeling/emulation, and/or machine learning
• Excellent programming skills (preferably Matlab, or Python)
• Strong analytical and problem-solving abilities
• Excellent communication and scientific writing skills, and fluency in English (both written and spoken)
• Enthusiasm for pursuing cutting-edge research within an international, multicultural collaborative team

Within the Department of Civil, Environmental and Geomatic Engineering, the Chair of Risk, Safety and Uncertainty Quantification offers an exciting opportunity to join a small, highly international research group of around 12 members. We foster a collaborative, supportive atmosphere where open scientific exchange and mutual respect are key. Our working culture is flexible and results-oriented, offering you the freedom to manage your schedule in a way that supports both productivity and personal well-being. Regular group discussions, interdisciplinary collaborations, and a shared passion for research make this an inspiring environment for pursuing a PhD.

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish.

We look forward to receiving your online application including the following documents:

• Detailed curriculum vitae (CV)
• Motivation letter (explaining your interest in the position and relevant experience)
• Academic transcripts (Bachelor's and Master's degrees)
• Names and contact information (email and phone) of at least two references

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

For further information about the position (no applications), visit our website contact Prof. Dr. Bruno Sudret by email.

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.