Doctoral Student - Risk-Based Inspection and Maintenance Planning of Tunnels

About the role

The Chair of Infrastructure Management at ETH Zurich's Institute of Construction and Infrastructure Management is looking for a doctoral student to work on risk-based inspection and maintenance planning for tunnels. The project, funded by the SNSF, is interdisciplinary and runs in collaboration with the Free University of Bozen-Bolzano, the Hagerbach Test Gallery and the EuroTube Foundation. It focuses on probabilistic risk updating, Value of Information analysis and intervention program optimisation. The preferred start date is 1 February 2027, though other dates are negotiable.

Key responsibilities

• Map and formalise current tunnel maintenance decision processes for structural and mechanical, electrical and plumbing domains through workshops and interviews with practitioners.
• Formulate risk and consequence models based on Bayesian networks and develop incremental risk updating methods.
• Develop a computationally tractable Value of Information analysis using approximation strategies such as surrogate models and scenario reduction.
• Formulate and solve intervention program optimisation models accounting for sequencing, bundling, access windows and resource constraints, with outputs traceable to the underlying evidence.
• Contribute to validation campaigns at the Hagerbach Test Gallery and the EuroTube Foundation.

Required profile

• Master's degree in civil engineering, operations research, industrial engineering, applied mathematics or a related field.
• Strong background in probabilistic modelling, reliability and risk analysis, and mathematical optimisation.
• Familiarity with infrastructure asset management, Bayesian networks, decision analysis, Value of Information analysis or business process modelling is an advantage.
• Willingness to carry out field work in underground environments and to collaborate in an interdisciplinary, international team.
• Proficiency in written and spoken English; German is an asset.

Required skills

• Very good programming skills, preferably in Python.
• Experience with optimisation solvers.

Applications must be submitted online by 9 November 2026, with a letter of interest, a CV, grades and diplomas, and contact details of at least two referees. Screening starts on 9 November 2026 and continues until the position is filled.