PhD positions: Data-driven approaches in solid mechanics

The Computational Mechanics Group in the Department of Mechanical and Process Engineering of ETH Zurich is seeking two doctoral students for a project on data-driven approaches in solid mechanics. The positions are funded by a recently awarded SNF Grant of Prof. Laura De Lorenzis.

Project background

The project aims at exploiting modern tools of machine learning, including sparse regression, sparse Bayesian learning and deep learning, for the fully automated discovery of material models, or for the encoding of these models in deep neural networks based on data measurable with mechanical testing setups. This will lead to substantial savings in the time and cost of identification procedures, as well as to greater objectivity and robustness of the related results. It will also deliver powerful tools for scientific discovery.

Job description

You will have the unique opportunity to learn, develop and apply a range of cutting-edge modeling, computational and experimental techniques, including computational mechanics tools, machine learning tools, digital image and digital volume correlation, and X-ray microtomography.

You will work in an interdisciplinary, cutting-edge, fast-paced research environment, interact with researchers with different specializations, gain skills in computational and experimental technologies, and interact with world-class collaborators.

Your profile

• Recently obtained master degree in engineering or physics with outstanding grades
• Excellent knowledge of continuum mechanics and finite element analysis
• Knowledge and experience with machine learning tools are a plus for both positions
• Knowledge and experience with phase-field modeling of fracture are a plus for one of the positions
• A keen interest in bridging machine learning tools with solid mechanics knowledge

Interested?

We look forward to receiving your online application with the following documents:

• CV
• full transcripts from undergraduate studies (both Bachelor and Master)
• a brief (1-page) motivation letter
• at least 2 names of referees (with email addresses)

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about the Department of Mechanical and Process Engineering can be found on our website. Questions regarding the position should be directed to Prof. Laura De Lorenzis at ldelorenzis@ethz.ch (no applications).