Doctoral Position in Uncertainty-Aware Condition Assessment of Tunnels

The Chair of Infrastructure Management at ETH Zurich invites applications for a doctoral position focused on transforming heterogeneous inspection and monitoring evidence into uncertainty-aware condition indicators for tunnels. Working within an interdisciplinary SNSF project, the successful candidate will fuse multi-source inspection data, such as images, videos, laser scans, and sensor readings, using hierarchical Bayesian methods. The research involves close collaboration with academic and industry partners, including fieldwork at test facilities in Switzerland. The candidate is expected to contribute to digital twin prototyping, sensor data processing, and validation campaigns.

• Develop automated methods to extract defect and deterioration indicators from inspection images, videos, sensor feeds, and 3D laser scans.
• Implement domain adaptation techniques for tunnel environments and perform time-lapse change detection.
• Fuse multi-source evidence into asset-level condition indicators using a hierarchical Bayesian framework.
• Formulate rigorous uncertainty models and evidence quality flags for diverse data sources.
• Contribute to defining condition indicator types within the digital asset model and integrate them into the digital twin prototype.
• Conduct field work and validation campaigns at the Hagerbach Test Gallery and DemoTube facilities.