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, X-Ray systems, etc. The research focus of the high-voltage laboratory is on technologies of components for the future electric power transmission system.
The project is funded by the Swiss Federal Office of Energy SFOE within the Program Grids and involves also the collaboration with Hitachi ABB Power Grids 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 acquisition and processing technology with deep learning methods. Gas circuit breakers will be used as an example case study.
The methodology will be first developed and tested under laboratory conditions with circuit breakers over-equipped with sensors. Data acquisition shall use IoT platforms for minimal energy use and large-scale deployment in laboratory and substation environments. Based on the collected multi-modal condition monitoring data, a set of algorithms for predictive maintenance will be developed combining signal processing and deep learning methods that enable to combine information from different data sources and to transfer the operational experience between laboratory experiments and real operation and also to transfer the developed models between different assets. 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 or learning theory and should have own practical experience in setting up, automating and/or conducting experiments (hardware related). Professional command of English (both written and spoken) is mandatory.
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We look forward to receiving your online application including a letter of motivation, your CV, a brief research statement (what do you think would be relevant in the context of the open position and how would you solve the problem, 1 page), one publication (e.g. your thesis or a conference or journal publication), transcripts of all obtained degrees (in English with individual courses and grades). 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 soon as possible and 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).