A chair at the crossroads of materials and artificial intelligence

Designing new materials has never been more complex. Researchers have to consider a mind-boggling array of factors, from chemical composition and processes to costs, environmental impacts, and the target properties—and they don’t always have the tools they need to do so. The Machine Learning for Material Design & Efficient Systems Chair led by MIAI* will provide a response. The Chair involves five industrial partners, which include ArcelorMittal and Total, and three government research labs, including Grenoble Institute of Technology-Phelma’s SIMaP lab and LIG**.

The four-year Chair will encompass several PhD and post-doc research projects, develop course content for students and Grenoble Institute of Technology’s Phelma engineering school, and collaborations with the industrial partners on utilizing the data from instrumented manufacturing processes and on promising materials for massive carbon capture.

Contacts: alexis.deschamps@simap.grenoble-inp.fr ; massih-reza.amini@univ-grenoble-alpes.fr

*Multidisciplinary Institute in Artificial Intelligence, Grenoble  

**Grenoble IT lab

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