The Chair of Infrastructure Management at the Institute of Construction and Infrastructure Management, Department of Civil, Environmental and Geomatic Engineering, offers a doctoral position focused on transforming heterogeneous inspection and monitoring evidence into uncertainty-aware condition indicators for tunnel structures and mechanical, electrical, and plumbing systems within a digital twin. 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, with other dates negotiable.
Tunnels are critical links in transport networks, and many Swiss road and rail tunnels are ageing while facing increasing traffic loads, climate-related stressors, and evolving safety requirements. The project Predictive, Resilient, Integrated Systems for Management of Tunnel (PRISM-T), funded by the SNSF, addresses these challenges through an integrated digital twin workflow for tunnel maintenance decision support. 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 are integral to the doctoral research.
Tasks
- Develop methods to extract defect and deterioration indicators from inspection images, video, sensor data, and periodic 3D laser scans, including domain adaptation for tunnel environments and time-lapse change detection.
- Fuse evidence from multiple sources into asset-level condition indicators using a hierarchical Bayesian framework.
- Develop uncertainty models and evidence quality flags for each source.
- Contribute to defining condition indicator types in the digital asset model and integrate the indicators into the digital twin prototype.
- Participate in inspection and validation campaigns at the Hagerbach Test Gallery and the EuroTube Foundation.
Requirements
- A Master’s degree in civil engineering, geomatics, computer science, or a related field.
- A strong background in machine learning, computer vision, or 3D point cloud processing, as well as in probabilistic modelling and Bayesian inference.
- Very good programming skills, preferably in Python.
- Familiarity with digital twins, building information modelling (BIM) and IFC, sensor data processing, or tunnel inspection is 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.
Benefits
ETH Zurich is one of the world’s leading universities specialising in science and technology, renowned for excellent education, cutting-edge fundamental research, and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find the university to be a place that promotes independent thinking and inspires excellence. ETH Zurich values diversity and sustainability, encourages an inclusive culture, promotes equality of opportunity, and nurtures a working and learning environment where the rights and dignity of all staff and students are respected. Sustainability is a core value, with consistent efforts towards a climate-neutral future.
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Das Team
The position is within the Chair of Infrastructure Management, led by Professor Dr. Bryan T. Adey, and involves close collaboration with interdisciplinary partners including the Free University of Bozen-Bolzano, the Hagerbach Test Gallery, and the EuroTube Foundation. Regular field work and teamwork with the partner team in Bolzano are integral to the research.