PhD Student in Intelligent Maintenance of Gas Circuit Breakers
The PhD student will be jointly supervised by two chairs: Chair of Intelligent Maintenance Systems and High Voltage Laboratory.
The Chair of Intelligent Maintenance Systems focuses on developing intelligent algorithms to improve performance, reliability and availability of complex industrial assets and making the maintenance more cost efficient. Our research focuses on deep learning, domain adaptation, hybrid approaches (combing physical performance models and deep learning algorithms), and deep reinforcement learning. The data we are typically dealing with comprises heterogeneous multivariate time series data of different types, with different sampling rates and different degrees of uncertainties.
The task of high-voltage engineering is to control and master high electric field strengths. Its prime use is in electric power transmission and distribution, but numerous technical applications are based on high voltages: light and laser technology, particle accelerators, flue gas cleaning systems, powder coating, ozone generation, X-Ray systems, fragmentation technology, etc. The research focus of the high-voltage laboratory is on technologies of component for the future electric power transmission system.
The project is funded by Swiss Federal Office of Energy SFOE within the Program Grids and involves also the collaboration with ABB and BKW.
The main objective of the PhD project is to develop advanced and robust non-intrusive technologies for predictive maintenance of components in the electric power system combining novel signal processing and deep learning methods. Gas circuit breakers will be used as an exemplary case study.
The methodology will be first developed and tested under laboratory conditions with circuit breakers over-equipped with sensors. The impact of combining data sources from several sensors will be evaluated by developing multi-modal deep learning algorithms. In the final step, the methodology will be tested under real operating conditions.
The position combines design of experiments, selection and implementation of sensors, automation of experiments and data collection, signal processing and development of deep learning algorithms.
We are looking for a PhD candidate with a strong analytical background and experimental experience, and an outstanding MSc degree in Electrical, Civil or Mechanical Engineering, Physics, or a related field. The candidate should be proficient in machine learning, deep learning, signal processing, statistics and learning theory and should have own practical experience in setting up, automating and/or conducting experiments. Professional command of English (both written and spoken) is mandatory.
We look forward to receiving your online application including a letter of motivation, a CV, a brief research statement (1 page), one publication (e.g. thesis or preferably a conference or journal publication), transcripts of all obtained degrees (in English with individual courses and grades), and contact details of two potential references, and letters of reference or recommendation (only if available, e.g. from internships, no need to request new ones at this stage of the application process). 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.
This position will be available as of October 2020 or upon agreement; the planned project duration is four years.
For more information about the two chairs please visit: www.ims.ibi.ethz.ch and www.hvl.ee.ethz.ch. Questions regarding the position should be directed to Prof. Dr. Olga Fink or Prof. Dr. Christian Franck by email firstname.lastname@example.org / email@example.com (no applications).