Doctoral Position in Uncertainty-Aware Condition Assessment of Tunnels

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

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. This position focuses on transforming heterogeneous inspection and monitoring evidence into uncertainty-aware condition indicators for tunnel structures and mechanical, electrical, and plumbing systems within a digital twin. This position is part of an interdisciplinary SNSF project in collaboration with the Free University of Bozen-Bolzano, the Hagerbach Test Gallery, and the EuroTube Foundation.

The preferred starting date is 1 February 2027, although other dates are negotiable.

Project background

Tunnels are critical links in transport networks, and many Swiss road and rail tunnels are ageing while being subject to increasing traffic loads, climate-related stressors, and evolving safety requirements. Their maintenance involves two interdependent asset domains, namely structural elements, such as lining, portals, and drainage, and mechanical, electrical, and plumbing systems, such as ventilation, lighting, and fire detection. In current practice, these domains are managed largely in isolation, inspections remain heavily manual, and evidence from different campaigns, sensors, and specialists is fragmented across separate reports and databases. Interventions are therefore often planned using disjointed evidence and conservative heuristics, even where substantial data exist.

The project Predictive, Resilient, Integrated Systems for Management of Tunnel (PRISM-T), funded by the SNSF, addresses this challenge through an integrated digital twin workflow for tunnel maintenance decision support. Heterogeneous inspection and monitoring evidence is transformed into uncertainty-aware condition indicators, which are used to update risk, to assess whether additional information is worth collecting, and to derive executable intervention programs. Extended reality (XR) is used to validate decisions and to guide inspection and maintenance work on site.

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.

Within this project, the doctoral position addresses the evidence layer of the workflow, in which inspection images, 3D laser scans, and sensor data are transformed into condition indicators that form the basis for the risk updating and intervention planning carried out in a companion doctoral position.

Job description

The successful candidate will 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. Evidence from multiple sources will be fused into asset-level condition indicators using a hierarchical Bayesian framework, and uncertainty models and evidence quality flags will be developed for each source. The candidate will contribute to defining the condition indicator types in the digital asset model, to integrating the indicators into the digital twin prototype, and to inspection and validation campaigns at the Hagerbach Test Gallery and the EuroTube Foundation.

Profile

• 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

We offer

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

Working, teaching and research at ETH Zurich

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.

About ETH Zürich

ETH Zurich is one of the world’s leading universities specialising inscience and technology. We are renowned for our excellent education,cutting-edge fundamental research and direct transfer of new knowledgeinto society. Over 30,000 people from more than 120 countries find ouruniversity to be a place that promotes independent thinking and anenvironment that inspires excellence. Located in the heart of Europe,yet forging connections all over the world, we work together todevelop solutions for the global challenges of today and tomorrow.