Doctoral Position in Robust Railway Intervention Planning under Uncertainty

This doctorate aims to develop uncertainty-aware methods and optimisation algorithms for robust intervention planning and resource forecasting in railway infrastructure management. Characterising and propagating the principal sources of uncertainty in intervention planning and resource usage across multiple time horizons and asset categories. Extending mixed-integer linear programming models through stochastic and robust optimisation to compute robust intervention programs. Contributing to the design of a GIS-based decision-support platform that integrates asset data, intervention plans, and uncertainty analytics. Validating the developed methods against historical and planned SBB corridor data.

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 the development of uncertainty-aware methods and optimisation tools for robust railway intervention planning and resource forecasting. The position is connected to the interdisciplinary ETH Mobility Initiative research project conducted in collaboration with the SBB. 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.

• A Master’s degree in civil engineering, transport engineering, operations research, applied mathematics, computer science, or a closely related discipline

• A strong background in mathematical optimisation, in particular mixed-integer linear programming, together with stochastic or robust optimisation and solid programming skills (preferably in Python)

• Familiarity with uncertainty quantification, railway or infrastructure asset management, or geographic information systems is considered an advantage

• Proficiency in written and spoken English is required, and knowledge of German is regarded as an asset given the collaboration with Swiss project partners