Postdoctoral Researcher in Data-Driven Predictive Maintenance for Power Grids in Zürich

100%, Zurich, temporary

The Reliability and Risk Engineering Laboratory within the Institute of Energy and Process Engineering at ETH Zurich is inviting applications for a post-doctoral researcher (1.5 year contract) to work on the development of machine learning approaches to develop data-driven predictive maintenance algorithms for the components of electric power networks. This challenging project, requiring solid analytical and machine learning skills, has for purpose to improve decision support systems for predictive-maintenance of electric power grids. The project is carried out in cooperation with swissgrid AG, which is the Swiss transmission system operator, and has high potential for transfer to power system practice.

We are looking for a highly motivated candidate with excellent programming skills (preferably Python and Matlab). The successful candidate has strong analytical skills, is proactive, self-driven with strong problem solving abilities and out-of-the-box thinking. Professional command of English (both written and spoken) is mandatory. 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.

To work on this interdisciplinary project, you will be part of the Reliability and Risk Engineering Laboratory of ETH Zurich. You will work in a highly stimulating environment with state-of-the-art computational infrastructure. This project will provide you with unique opportunities to develop a strong interdisciplinary portfolio in both machine learning and electric power system operations.

A competitive salary will be offered as well as other attractive working conditions: . Evaluation of candidates will begin immediately and continue until the opening is filled.

Your major objectives will be to develop predictive maintenance algorithms in order to determine the maintenance windows of power system assets; and apply these findings to optimize the maintenance schedule of grid assets with respect to grid operations. The results of your work will be experimentally validated using condition monitoring data of field equipment.

To meet the requirements you should hold a PhD degree in a field related to predictive maintenance/machine and deep learning/electric power systems/signal processing and have experience in deep learning applications. Preferably, you have gained some experience in maintenance and degradation modelling, optimization/decision-making and basic knowledge of electric power systems.

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.