Doctoral Position in Risk-Based Inspection and Maintenance Planning of Tunnels
Full-time
Zürich, Switzerland
Doctoral Position in Risk-Based Inspection and Maintenance Planning of Tunnels
100%, Zurich, fixed-term
The Chair of Infrastructure Management, led by Professor Dr. Bryan T. Adey within the Institute of Construction and Infrastructure Management of the Department of Civil, Environmental and Geomatic Engineering, has an opening for a doctoral student. This position focuses risk-based inspection and maintenance planning for tunnels, including probabilistic risk updating, Value of Information analysis, and intervention program optimisation. This position is part of an interdisciplinary SNSF project in collaboration with the Free University of Bozen-Bolzano, the Hagerbach Test Gallery, and the EuroTube Foundation.
The preferred starting date is 1 February 2027, although other dates are negotiable.
Project background
Tunnels are critical links in transport networks, and many Swiss road and rail tunnels are ageing while being subject to increasing traffic loads, climate-related stressors, and evolving safety requirements. Their maintenance involves two interdependent asset domains, namely structural elements, such as lining, portals, and drainage, and mechanical, electrical, and plumbing systems, such as ventilation, lighting, and fire detection. In current practice, these domains are managed largely in isolation, inspections remain heavily manual, and evidence from different campaigns, sensors, and specialists is fragmented across separate reports and databases. Interventions are therefore often planned using disjointed evidence and conservative heuristics, even where substantial data exist.
The project Predictive, Resilient, Integrated Systems for Management of Tunnel (PRISM-T), funded by the SNSF, addresses this challenge through an integrated digital twin workflow for tunnel maintenance decision support. Heterogeneous inspection and monitoring evidence is transformed into uncertainty-aware condition indicators, which are used to update risk, to assess whether additional information is worth collecting, and to derive executable intervention programs. Extended reality (XR) is used to validate decisions and to guide inspection and maintenance work on site.
The workflow is developed and validated in the Tunnel Digitalisation Centre of the Hagerbach Test Gallery in Flums, and its transferability is tested at the DemoTube facility of the EuroTube Foundation in Dübendorf. Regular field work at both sites and close collaboration with the partner team in Bolzano form an integral part of the doctoral research.
Within this project, the doctoral position addresses the decision layer of the workflow, in which maintenance decision processes are formalised and uncertainty-aware condition indicators, developed in a companion doctoral position, are used to update risk and to optimise intervention programs under realistic tunnel constraints.
Job description
The successful candidate will map and formalise current tunnel maintenance decision processes for the structural and mechanical, electrical, and plumbing domains through workshops and interviews with practitioners. On this basis, risk and consequence models based on Bayesian networks will be formulated, and incremental risk updating methods will be developed. A computationally tractable Value of Information analysis will be developed using approximation strategies, such as surrogate models and scenario reduction. Intervention program optimisation models accounting for sequencing, bundling, access windows, and resource constraints will be formulated and solved, with outputs traceable to the underlying evidence. The candidate will contribute to validation campaigns at the Hagerbach Test Gallery and the EuroTube Foundation.
Profile
- A Master’s degree in civil engineering, operations research, industrial engineering, applied mathematics, or a related field
- A strong background in probabilistic modelling, reliability and risk analysis, and mathematical optimisation
- Very good programming skills, preferably in Python, and experience with optimisation solvers
- Familiarity with infrastructure asset management, Bayesian networks, decision analysis, Value of Information analysis, or business process modelling would be considered an advantage
- Willingness to carry out field work in underground environments and to collaborate within an interdisciplinary and international team
- Proficiency in written and spoken English is required, and knowledge of German is regarded as an asset
Workplace
Workplace
We offer
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.
We value diversity and sustainability
Curious? So are we.
We look forward to receiving your online application by 9 November 2026 including the following documents:
- Letter of interest including your ideas of potential research in the project
- A curriculum vitae (with list of publications, if applicable, and contact information of at least two referees)
- Grades of all university courses taken as well as diplomas
Screening of applications starts on 9 November 2026. Applications will be accepted until the position is filled.
Further information about the Institute of Construction & Infrastructure Management can be found on our website. Questions regarding the position should be directed to Ms. Nathalie Dietrich, dietrich@ibi.baug.ethz.ch (no applications).
Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.