Postdoctoral Researcher in Deep Learning for Time Series
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 successful applicant will drive the research in the field of deep learning applied to time series data of complex industrial systems. The position includes independent supervision of master students and involvement in the supervision of PhD students. Limited teaching responsibilities are also included in this position. We expect the candidate to be self-driven with strong problem solving abilities and out-of-the-box thinking. The duration of the post-doctoral appointment is foreseen for a minimum of one year.
We are looking for a researcher with a strong analytical background, and an outstanding doctoral degree in Engineering, Control, Computer Science, Physics, Applied Mathematics, or a related field. A candidate should be proficient in machine learning, deep learning, signal processing, statistics and learning theory.
We look forward to receiving your online application including a letter of motivation, a CV, a publication list, a brief statement of research interests (1 page), transcripts of all obtained degrees (in English), 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.
The review of applications will continue until the position is filled, with the position to start in autumn 2020.
For more information about the chair please visit: www.ims.ibi.ethz.ch. Questions regarding the position should be directed to Prof. Dr. Olga Fink by email email@example.com (no applications).