Tuesdays, October 14 - November 11, 2025
3:00-4:30 p.m. CT

The ability of artificial intelligence (AI) to successfully learn from large datasets has transformed science and engineering as we know it. AI can accelerate scientific discovery and innovation but often requires more computing power than is available to most researchers. The DOE provides supercomputers to solve the nation’s biggest scientific challenges, and this series aims continue to deepen and expand the AI knowledge base for the next generation of AI practioners.

Building on ALCF’s series Intro to AI-Driven Science on Supercomputers, we are hosting a series of hands-on courses that will expand upon advanced topics in AI for science. Attendees will deepen their understanding of applying AI at scale, learn about coupling science simulations with AI, dig into inference workflows, and explore how AI accelerators are enabling for AI for Science.

Pre-requisites

This training series is aimed at undergraduate and graduate students enrolled at U.S. universities. Attendees are expected to: 

• Have foundational knowledge of Python
• Have either attended the ALCF Intro to AI-Driven Science on Supercomputers series or have familiarity with the topics covered in the series
  • Topics include foundational concepts in parallel computing, neural networks, large-language models, prompt engineering, AI accelerators
  • Those without this background knowledge are welcome to look through the Intro to AI videos and materials before the start of the series.

Workshop Series Format

Each session will have both lecture and hands-on components, along with a talk from an Argonne scientist about the work they do using AI for their science.

Each session occurs on Tuesdays from 3:00-4:30 p.m. CT. Session recordings will be made available shortly after each session.

Attendees who complete all in-class and post-class exercises by the end of the series will receive a certificate of completion and a digital badge.

Session materials are hosted on the ALCF AI Science Training series GitHub [click here].

Recordings for each session will be posted weekly on the session-specific pages below.

Registration 

The deadline to register is September 30, 2025. The series is free to attend, but registration is required. Attendees will receive the link to the webinar once they have registered.

Please register if you are able to commit to attending all 5 sessions. For those who cannot commit to all sessions, materials and session recordings will be made publicly available after each session. Registration restrictions may be enforced at our discretion.

To register, please visit here.