PhD in Uncertainty-Aware Tunnel Condition Assessment

NZZone; Zürich; Hybrid; CHF 60,000 - 72,000; Full time

Job summary

ETH Zurich seeks a doctoral researcher to advance an integrated digital twin workflow for tunnel maintenance, transforming inspection and monitoring data into uncertainty-aware condition indicators that guide risk updates and intervention planning within the PRISM-T project.

The role involves developing Bayesian data fusion methods, validating indicators on field data at partner sites, and collaborating with an international team to advance BIM/IFC integration and XR validation for decision

Qualifications

• Master’s degree in a relevant field and strong background in ML/vision.
• Experience with probabilistic modelling and Bayesian inference.
• Proficient in Python and software development best practices.

Responsibilities

• Develop methods to extract defect and deterioration indicators from inspection images, video, sensor data and 3D laser scans.
• Fuse evidence from multiple sources into asset-level indicators using a hierarchical Bayesian framework.
• Develop uncertainty models and evidence quality flags for each data source.
• Contribute to defining the condition indicator types in the digital asset model and integrate into the digital twin prototype.

Skills

Machine learning

Computer vision

3D point cloud processing

Probabilistic modelling

Bayesian inference

English proficiency

Education

Master’s degree in civil engineering, geomatics, computer science

Tools

Python

Job description

ETH Zurich seeks a doctoral researcher to advance an integrated digital twin workflow for tunnel maintenance, transforming inspection and monitoring data into uncertainty-aware condition indicators that guide risk updates and intervention planning within the PRISM-T project.

The role involves developing Bayesian data fusion methods, validating indicators on field data at partner sites, and collaborating with an international team to advance BIM/IFC integration and XR validation for decision