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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 main objective of the PhD project is to develop methodology that enables fusing physical models and deep learning algorithms for optimal operation and maintenance of complex industrial systems. The research will build upon our previous research in this field and will develop the methodology further. 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.
We are looking for a PhD with a strong analytical background, and an outstanding MSc degree in Engineering, Control, Computer Science, Physics, Applied Mathematics, or a related field. The candidate should be proficient in machine learning, deep learning, signal processing, statistics and learning theory. Experience in reinforcement learning is beneficial. 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 statement of research interests (1 page), one publication (e.g. thesis or preferably a conference or journal publication), 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.
This position will be available as of September 2020 or upon agreement; the planned project duration is three years.
For more information about the chair please visit: . Questions regarding the position should be directed to Prof. Dr. Olga Fink by email firstname.lastname@example.org (no applications).
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