Doctoral Position in Risk-Based Inspection and Maintenance Planning of Tunnels
Develop advanced digital twin workflows and risk models for optimized tunnel maintenance decision-making at ETH Zurich.
Best for:Best for civil engineering or operations research graduates with strong probabilistic modeling, Bayesian networks, and Python programming skills.
Overview
ETH Zurich invites applications for a doctoral position focusing on risk-based inspection and maintenance planning for tunnels within the Chair of Infrastructure Management. Supervised by Professor Dr. Bryan T. Adey, the researcher will formulate Bayesian network risk models, perform Value of Information analysis, and optimize intervention programs within an interdisciplinary SNSF-funded project. The role involves close collaboration with academic and industry partners, including field work at testing facilities in Switzerland. The successful candidate is expected to contribute to digital twin workflows and validation campaigns to support sustainable infrastructure management.
What you'll do
- Map and formalize current tunnel maintenance decision processes for structural and MEP domains through practitioner workshops and interviews.
- Formulate risk and consequence models using Bayesian networks and develop incremental risk updating methods.
- Develop computationally tractable Value of Information analysis using approximation strategies such as surrogate models and scenario reduction.
- Formulate and solve intervention program optimization models accounting for sequencing, bundling, access windows, and resource constraints.
- Contribute to validation campaigns and regular field work at the Hagerbach Test Gallery and the EuroTube Foundation.
Requirements
Degree required: Master’s degree in civil engineering, operations research, industrial engineering, applied mathematics, or a related field
Degree offered: PhD
Language: Proficiency in written and spoken English is required; knowledge of German is regarded as an asset
Instruction: English
- Strong background in probabilistic modelling, reliability and risk analysis, and mathematical optimisation
- Very good programming skills, preferably in Python
- Experience with optimisation solvers
- Willingness to carry out field work in underground environments
- Ability to collaborate effectively within an interdisciplinary and international team
Funding & terms
How to apply
Deadline: 2026-11-09 · open until filled
Interested candidates must submit their application exclusively through the online application portal by the specified deadline. The application package should include a letter of interest detailing potential research ideas, a curriculum vitae with contact information for at least two referees, and complete university transcripts and diplomas. Applications sent via email or postal services will not be considered.
- Letter of interest
- CV
- List of publications
- Contact information of at least two referees
- University course grades
- Diplomas
Contact
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