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100%, Zurich, temporary
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.
ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.Working, teaching and research at ETH Zurich
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