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PhD Student in Engineering, Control, Computer Science, Physics, Applied Mathematics

ETH Zurich, Switzerland

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, at ETH Zurich, Switzerland

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General Info

Position: PhD Student
No. of Positions: 1
Research Field: , , ,
Joining Date: ASAP
Contract Period: 3 Years
Salary: 4'460 CHF/Month

Chair of Intelligent Maintenance Systems
ETH Zurich
ETH Zurich, Switzerland
Zurich , Switzerland

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Qualification Details

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.

Responsibilities/Job Description

The main objective of the PhD project is to develop physics-informed deep learning algorithms for system condition 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.

How to Apply?

Online Application through "Apply Now" Button from this page

Reference Number: -
(If any, use it in the necessary place)

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Documents Required

  • 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.

About the Project

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".

About the Employer: ETH Zurich, Switzerland

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Contact details

For more information about the chair please visit: Questions regarding the position should be directed to Prof. Dr. Olga Fink by email (no applications).

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