Doctoral Position in Uncertainty-Aware Condition Assessment 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 turn heterogeneous inspection and monitoring evidence into uncertainty-aware condition indicators for tunnels and their mechanical, electrical and plumbing systems, within a digital twin. The position is part of an interdisciplinary SNSF project (PRISM-T) with partners in Bolzano, the Hagerbach Test Gallery and the EuroTube Foundation, and is based in Zurich.

Key responsibilities

• 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, and build uncertainty models and evidence quality flags for each source.
• Contribute to defining condition indicator types in the digital asset model and integrate them into the digital twin prototype.
• Take part in inspection and validation campaigns at the Hagerbach Test Gallery and the EuroTube Foundation.

Required profile

• Master's degree in civil engineering, geomatics, computer science or a related field.
• Strong background in machine learning, computer vision or 3D point cloud processing, and in probabilistic modelling and Bayesian inference.
• Willingness to carry out field work in underground environments and to work in an interdisciplinary, international team.
• Written and spoken English; German is an asset.

Required skills

• Very good programming skills, preferably in Python.
• Advantageous: familiarity with digital twins, BIM and IFC, sensor data processing or tunnel inspection.

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.