Doctoral Position in optimization of rail and rolling stock maintenance processes with extended...

Doctoral Position in optimization of rail and rolling stock maintenance processes with extended reality and digital twins

The Chair of Infrastructure Management led by Professor Dr Bryan T. Adey in the Institute of Construction and Infrastructure Management of the Department of Civil, Environmental and Geomatic Engineering has an opening for a doctoral student in the effective adaptation of mixed reality and digital twins for rail and rolling stock maintenance.

In partnership with the SBB Center of Competence for Extended Reality and through the ETH Mobility Initiative, Professor Dr Bryan T. Adey will lead a team focused on the Optimization of Maintenance Processes with Extended Reality and Digital Twins. This is the OptXR project. His co-applicant, Prof. Marc Pollefeys, has two concurrent roles, which are directly relevant to this project. He is (1) a Professor of Computer Science at ETH Zurich and (2) the Director of the Microsoft Mixed Reality and AI Lab in Zurich. Prof. Pollefeys will supervise an additional doctoral student supporting this project. Professor Carl Haas from the University of Waterloo will serve as a co-investigator.

Project background

Efficient maintenance of rolling stock conducted in a timely and reliable manner is necessary for effective rail infrastructure and asset management. This is critical to ensure the safety and punctuality of railway operations including passenger traffic. Yet growing challenges need to be addressed to further ensure efficient maintenance: accurately monitor the performance of the assets in the face of increasing technical complexity; optimize and rationalize costly maintenance operations by anticipating the lifetime of assets, systematic failures or possible breakdowns; and, assist frontline maintenance workers who must carry out the required maintenance operations safely and efficiently under pressure, often with limited training and knowledge of specific types of assets or their operating histories. Digital twins (DTs) are powerful tools that can virtually model physical railway assets based on large amounts of data to accurately reflect in real-time and predict the behaviour of these assets.  DTs combined with Extended Reality (XR) will help frontline workers and technical experts to understand and visualize: (1) specific individual assets’ complex electromechanical subsystems’ workings, specifications, and sensed status to troubleshoot current malfunctions, and (2) specific individual assets’ future performance, to intervene with timely maintenance to avoid future malfunctions, thus avoiding both reactive maintenance and unnecessary scheduled maintenance.

With this vision in mind, OptXR will develop knowledge, processes and tools to improve the security and punctuality of maintenance processes with digital twins (DTs) and extended reality (XR). To achieve this overall goal, several objectives must be met:

Design software and hardware operating system architecture to seamlessly integrate DTs, XR, and diagnostics for real-time worker support operations

Building on SBB’s related experience with external door and coupler closed systems, rapidly prototype and test DTs for these highly relevant and complex asset classes, using a scalable diagnostics system architecture including a context-aware intuitive, simple and effective XR user interface

Conduct controlled experiments with partners at SBB in SBB facilities to validate the improved performance of maintenance activities with OptXR support

Recommend steps for implementation and large-scale deployment

OptXR will move SBB an important step toward achieving the practical benefits of digitalization using DTs and XR, in terms of optimization and rationalization of maintenance operations without compromising the security and punctuality of the railway traffic. Positive and substantial by-products will be on-the-job immersive training, as well as knowledge management and transfer to overcome strong fluctuations in human resources.

Job description

You are expected to work collaboratively with a doctoral student in Computer Science, with the Professors, and with the SBB’s Center of Competence for Extended Reality (CoC XR), Center of Competence for Predictive Maintenance (CoC PM), and Reliability Centered Maintenance (RCM) division. SBB will provide the knowledge and expertise that the research team will use to build powerful Digital Twins (DTs) that drive a context-aware, intuitive, simple and effective XR user interface for the maintenance workers. Such a system must build on SBB’s current asset performance monitoring activities, and it must leverage a large amount of existing data by facilitating the gathering, combining, organizing, correlating, and analyzing of data from failures, sensors, and logs of frontline workers. Outcomes of the project should be measurable improvements in the reliability and cost-effectiveness of rolling stock maintenance processes.

Our vision is that workers will be assisted with relevant, context-aware, useful, step-by-step maintenance instructions through an XR kit with overlaid graphics, text boxes, hazard warnings, and live DT data such as heat maps. This will be supported by back-end process optimization; accurately monitoring the performance of assets will enable optimal planning, predictive maintenance, and effective operations. Workers will be guided by such back-end information, recorded experiences of previous maintenance crews with the system being repaired, recent events experienced by the system while it was under operation, and bespoke predictive maintenance recommendations. Ultimately, demanding maintenance operations will be optimized and rationalized without compromising the security and punctuality of railway operations.

Primary intellectual contributions made by the doctoral student supervised by Professor Adey will include (1) generalizable methodologies for effectively building on and transforming the large amount of data and expertise available at the SBB into a DT framework, and (2) testing the potential for improved security and punctuality of rolling stock maintenance processes based on well-designed experiments involving human subjects and research ethics approval.  

Your profile

You have a Master's degree in transport planning, civil engineering, mechatronics engineering, systems engineering or a related field. They will have a combination of knowledge and experience in a subset of infrastructure management theory and practices, asset management, deterioration modelling, mixed reality, relevant aspects of AI such as automated diagnostics and computer vision, design of experiments involving human subjects, probability theory, risk assessment, R, and python. A proven ability to learn quickly and fill in knowledge gaps is required. Good knowledge of English and good writing skills are essential. Experience with the Hololens and Unity-related development platforms and associated SDKs would be beneficial.

We offer

ETH Zurich is a family-friendly employer with excellent working conditions. You can look forward to an exciting working environment, cultural diversity and attractive offers and benefits.

We value diversity

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.

Curious? So are we.

We look forward to receiving your online application with the following documents:

• A letter of interest including your understanding of the problem and thoughts on a way forward
• A publication in which you were the first author.
• A curriculum vitae (with a list of publications and contact information of at least two referees).
• Grade records of all university courses taken as well as diplomas.

Screening of applications starts on the 24th of October 2022. Applications will be accepted until the position is filled.

Starting date: The preferred start date is the 1st of January 2023, although others are possible.

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

For further information about the position, please contact Ms Nathalie Dietrich by e-mail: [email protected] (no applications) and visit our website: www.ibi.baug.ethz.ch .

About ETH Zürich

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science and technology. We are renowned for our excellent education,
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