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 physics-informed deep learning algorithms for hybrid digital twins of complex industrial systems. The developed methodology will enable to combine the learning capabilities of machine learning algorithms with the interpretability and extrapolation abilities of physics-based approaches. 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 graph neural networks is beneficial. Professional command of English (both written and spoken) is mandatory.
We look forward to receiving your online application including (I) letter of motivation, (II) CV, (III) brief research statement describing your project idea relevant to the job description, making connection to your experience in this area and the related work from the literature, (IV) one publication (e.g. thesis or preferably a conference or journal publication), (V) transcripts of all obtained degrees (in English), (VI) contact details of at least 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 soon as possible or upon agreement; the planned project duration is three years.
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 firstname.lastname@example.org (no applications).
|Titel||PhD Student in Physics-Informed Deep Learning for Hybrid Digital Twins of Complex Industrial Systems|
|Job location||Rämistrasse 101, 8006 Zurich|
|Veröffentlicht||November 26, 2020|
|Jobart||PhD/ Doktorand/in  |
|Fachbereiche||Algorithmen,   Künstliche Intelligenz,   Artifizielles Neuronales Netz,   Steuerungstechnik,   Physikingenieurswesen,   Maschinelles Lernen,   Signalaufbereitung  |