Doctoral Position in Uncertainty-Aware Condition Assessment of Tunnels

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 develop uncertainty models and evidence quality flags for each source. Contribute to defining condition indicator types in the digital asset model, integrating the indicators into the digital twin prototype, and inspection and validation campaigns at the Hagerbach Test Gallery and the EuroTube Foundation.

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. The position is part of the interdisciplinary SNSF-funded project Predictive, Resilient, Integrated Systems for Management of Tunnel (PRISM-T), in collaboration with the Free University of Bozen-Bolzano, the Hagerbach Test Gallery, and the EuroTube Foundation. The project develops and validates 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 form an integral part of the doctoral research.

• 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 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