Doctoral Position in Uncertainty-Aware Condition Assessment of Tunnels

Develop uncertainty-aware tunnel condition assessment from images, video, sensors and repeated three-dimensional scans. Fuse deterioration evidence through hierarchical Bayesian models, represent evidence quality and integrate indicators into a digital-twin prototype. The project includes underground inspection and validation with Hagerbach Test Gallery and EuroTube. Screening starts 9 November 2026; applications remain open until filled. Apply through the ETH online recruitment portal.

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