PhD Student in Physics - Informed Deep Learning for System Health Monitoring and Prediction

PhD Student in Physics - Informed Deep Learning for System Health Monitoring and Prediction

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. We develop algorithms for system health monitoring and prediction of complex infrastructure and industrial assets. 

The PhD thesis will be embedded in the project funded by the Swiss National Science Foundation "Operational digital twins of complex industrial systems based on physics-informed deep learning with integrated structural inductive bias, physics and domain expertise". 

The main objective of the PhD project is to develop physics-informed deep learning algorithms for system health monitoring and prediction for complex infrastructure and industrial assets. 

Two PhD positions are available. One PhD position will be focusing on developing physics-informed graph neural networks for efficient real-time modelling of complex physical processes monitored by spatially distributed sensor networks. The second PhD position will be focusing on developing methodology for disentangled, physics-informed feature representation learning and integrating prior physical knowledge and domain expertise as regularizers in the learning process of physics-informed neural networks.

This position will be available as soon as possible or upon agreement; the planned project duration is three years.

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. You should be proficient in machine learning, deep learning, signal processing, statistics and learning theory. Experience in graph neural networks for the first PhD position is required. 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.

We look forward to receiving your online application until December 15, 2021 including:

• Letter of motivation
• CV
• Brief research statement (one page) describing your project idea in the field of physics-informed deep learning algorithms, making connection to your experience in this area and the related work from the literature
• One publication (e.g. thesis or preferably a conference or journal publication)
• Transcripts of all obtained degrees (in English)
• 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.

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 ofink@ethz.ch (no applications).

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website  to find out how we ensure a fair and open environment that allows everyone to grow and flourish.