PhD: Risk-Based Tunnel Maintenance & Digital Twin

NZZone; Zürich; Hybrid; CHF 45,000 - 55,000; Full time

Job summary

ETH Zurich's Chair of Infrastructure Management invites a doctoral candidate to map and formalize tunnel maintenance decision processes for structural and MEP domains, using Bayesian networks to update risk. You will develop Value of Information analyses and intervention program optimization, employing surrogate models and scenario reduction, with validation campaigns at partner sites in Switzerland.

The project is interdisciplinary and international, offering access to digital twin workflows,

Qualifications

• A Master’s degree in civil engineering or related field with strong probabilistic modelling background.
• Experience in reliability and risk analysis and mathematical optimisation is essential.
• Programming skills in Python and experience with optimisation solvers are required.
• Familiarity with Bayesian networks, decision analysis and Value of Information analysis is advantageous.
• Willingness to conduct field work in underground environments and collaborate in an international team.
• Fluent written and spoken English; German is a plus.

Responsibilities

• Map and formalise tunnel maintenance decision processes across structural and MEP domains.
• Formulate risk and consequence models using Bayesian networks and update methods.
• Develop Value of Information analysis and optimization of intervention programs.
• Contribute to validation campaigns at partner facilities and in the field.

Skills

Probabilistic modelling

Reliability and risk analysis

Mathematical optimisation

Python programming

Optimization solvers

Bayesian networks

Decision analysis

Value of Information

Business process modelling

Field work readiness

English proficiency

Education

Master’s degree in civil engineering

Tools

Python

Optimization solvers

Job description

ETH Zurich's Chair of Infrastructure Management invites a doctoral candidate to map and formalize tunnel maintenance decision processes for structural and MEP domains, using Bayesian networks to update risk. You will develop Value of Information analyses and intervention program optimization, employing surrogate models and scenario reduction, with validation campaigns at partner sites in Switzerland.

The project is interdisciplinary and international, offering access to digital twin workflows,