Community and Workforce Development

The Earth system science (ESS) community and workforce must be literate in AI fundamentals and skilled in ESS applications to harness technological innovations, advance research, and prepare for the future. Our community must also be trained in the best practices for FAIR data and open science to produce and use open, transparent, and reproducible AI/ML. Such training strengthens understanding of when conventional or AI/ML approaches are most appropriate for a given scientific challenge. Investment in an AI-skilled workforce is foundational to advancing ESS knowledge and strengthening national resilience to extreme and hazardous weather.

NSF NCAR convenes the ESS community to anticipate workforce needs by bringing together domain knowledge, AI/ML proficiency, and pedagogical expertise. We support the ESS community and workforce by delivering accessible, domain-focused training and resources, building institutional and community literacy in AI, and investing in professional development through AI/ML-focused internships and visitor opportunities.

AI/ML Training for the Earth System Science Community

NSF NCAR, in partnership with UCP, provides foundational AI/ML and LLM training, building knowledge through focused ESS applications developed by AI/ML, ESS, and educational expertise from across NSF NCAR and UCP.

We provide expertise-vetted educational material to staff and the ESS community through instructor-led courses and self-paced, asynchronous training modules.

Preview our machine learning bootcamp and curriculum.

View our Jupyter tutorials

EdEC Roundtable Discussion Series

The EdEC Roundtable Discussion Series supports professional development of staff working in education, training, and education research.

Staff learn from researchers and practitioners regarding the role of AI in higher education, the use of AI in scientific writing, and best practices using AI in research.

Student Internships for AI/ML in Earth System Science

NSF NCAR provides students with foundational and applied AI/ML research experience in ESS through our suite of internship and visitor opportunities across the organization. Such opportunities include the NSF NCAR Earth System Science Internship (NESSI) and CISL’s Summer Internships in Parallel Computational Science (SIParCS).

Explore our internships.

Community Trust in AI for Earth System Science

NSF NCAR leads and collaborates on trustworthy AI research and actively builds trust in AI for the ESS community and the public. We showcase such efforts in the Explorer Series and the upcoming Conversations with Scientists.

Read more about our scientific research into the concept of “trustworthiness” here.

Community of Practice

As a national center serving the research community, NSF NCAR is establishing a Community of Practice to share knowledge, tools, and resources and to co-develop best practices. Through open collaboration and learning from one another, we will strengthen collective capability and advance responsible, scientifically grounded AI for Earth system science.

More information coming soon!

Contact Us

For more information about NSF NCAR’s AI initiatives, to join our Community of Practice, or to explore opportunities to collaborate, please reach out to ncarai@ucar.edu. We look forward to advancing this work together.