Postdoctoral position within the project on integrated intelligent railway wheel condition prediction (INTERACT)

ETH Zurich is one of the world’s leading universities specialising in science and technology. It is renowned for its excellent education, its cutting-edge fundamental research and its efforts to put new knowledge and innovations directly into practice. The Chair of Intelligent Maintenance Systems at the Department of Civil, Environmental and Geomatic Engineering focuses on developing algorithms and decision support systems for data-driven intelligent maintenance of industrial assets.

Postdoctoral position within the project on integrated intelligent railway wheel condition prediction (INTERACT)

Railway wheels are safety critical components, have a significant impact on the performance and are a major cost driver for maintenance. The condition of the wheels also has a significant influence on the infrastructure condition and its maintenance. Furthermore, wheel defects cause noise and vibration emissions.
Due to their criticality, wheels are tightly monitored by different condition monitoring devices: including fixed installations, wayside monitoring devices and in‐workshop inspections. While wheel defect detection with wayside monitoring devices belongs to the state of the art, particularly based on strain gauges, prediction of the wheel deterioration and defect evolution in time under varying operating conditions is still an open research question.
The goal of the research project is to predict the evolution of the wheel condition in time by integrating the information of several heterogeneous data sources including real‐time information on wheel condition and influencing parameters of its deterioration. The developed methodology will be based on deep learning algorithms enabling to learn the relevant features and their relationships from the heterogeneous data sources and use the learnt relationships to predict the profile evolution.
The project is conducted in a close collaboration with the SBB (Swiss Federal Railways).

We are looking for a highly motivated candidate holding a PhD Degree in a field related to predictive maintenance, signal processing, machine and deep learning. The successful candidate has strong analytical skills, is proactive, self-driven with strong problem solving abilities and out-of-the-box thinking. Moreover, programming experience, preferably in Python, is expected. Experience in the field of railway systems is beneficial. Professional command of English (both written and spoken) is mandatory and a good knowledge in German is required. You enjoy working in an interactive international environment with doctoral students and other post-docs, referring continuously to practical problems and solutions and collaborating with industrial project partners.
Review of applications will continue until the position is filled, with the position to start as soon as possible and the planned project duration being two years.

We look forward to receiving your online application including a letter of motivation, CV, a brief statement of research interests (1 page), transcripts of all obtained degrees (in English), one publication (the one that you consider being most relevant to the position that you are applying for), and contact details of two referees. Only complete applications containing all the required documents will be considered. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

For more information about the chair, please visit: ims.ibi.ethz.ch. For more information about the research project please visit: www.sccer-mobility.ch/Transfer/ETH-Mobility-Initiative/Interact. Questions regarding the position should be directed to Prof. Dr. Olga Fink by email ofink@ethz.ch (no applications).


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