Doctoral Position in Uncertainty-Aware Tunnel Condition Assessment

The Chair of Infrastructure Management at ETH Zurich offers a doctoral position focused on translating heterogeneous inspection and monitoring data into reliable condition indicators for tunnel infrastructure. Working within an interdisciplinary SNSF-funded project, the successful candidate will fuse inspection images, 3D laser scans, and sensor streams using hierarchical Bayesian frameworks. The research involves close collaboration with academic and industry partners, alongside practical fieldwork at underground testing facilities in Switzerland. The appointee is expected to advance uncertainty-aware predictive modeling to support structural health monitoring and digital twin decision-making.

• Develop algorithms to extract defect and deterioration indicators from inspection images, videos, and sensor data.
• Implement time-lapse change detection and domain adaptation techniques suited for underground tunnel environments.
• Fuse evidence from multiple data sources into asset-level condition indicators using a hierarchical Bayesian framework.
• Formulate robust uncertainty models and quality assessment flags for each incoming data source.
• Contribute to defining condition indicator taxonomies and integrating them into digital twin prototypes.
• Participate in regular field inspection and validation campaigns at underground partner testing facilities.