# Collaborative approaches improve materials informatics workforce training

**URL:** <https://community.materialssquare.com/t/collaborative-approaches-improve-materials-informatics-workforce-training/418>\
**Category:** News\
**Created:** [June 19, 2024, 1:56pm UTC](https://community.materialssquare.com/t/collaborative-approaches-improve-materials-informatics-workforce-training/418 "2024-06-19T13:56:20Z")\
**Posts on this page:** 1\
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**Author:** ![gabriele1](https://yyz1.discourse-cdn.com/flex003/user_avatar/community.materialssquare.com/gabriele1/32/20_2.png) [@gabriele1](https://community.materialssquare.com/u/gabriele1)\
**Post date:** [June 19, 2024, 1:56pm UTC](https://community.materialssquare.com/t/collaborative-approaches-improve-materials-informatics-workforce-training/418/1 "2024-06-19T13:56:20Z")

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Researchers are enhancing materials informatics (MI) workforce training by integrating AI, machine learning, and computational methods with materials science. Three NSF NRT projects at the University of Chicago, Duke University, and the University of Illinois focus on cross-disciplinary and industry partnerships to improve graduate-level programs. These initiatives aim to provide students with an interdisciplinary mindset and the necessary skills for MI research, fostering collaboration and resource sharing to support collective goals and advance MI education.

For more details, read the full article here:

> **[Collaborative approaches improve materials informatics workforce training](https://phys.org/news/2024-06-collaborative-approaches-materials-informatics-workforce.html)**
>
> A wide range of innovative products, from biomedical implants to aerospace composites, come from research in materials informatics (MI)—the combination of machine learning, artificial intelligence and computational methodologies with materials...