By using machine learning as an image processing technique, scientists can dramatically accelerate the heretofore laborious manual process of quantitatively looking for and at interfaces without having to sacrifice accuracy.
Students from the Roy and Marcia Armes Engineering Leadership Institute at the University of Toledo visited the U.S. Department of Energy’s (DOE) Argonne National Laboratory and discovered Argonne’s engaging STEM leadership pathways.
The U.S. Department of Energy’s Argonne National Laboratory’s Educational Programs and Outreach department hosted Computer Science for All — Coding and Beyond, in December as a part of the Argonne National Laboratory, Chicago, initiative.
A white paper from the International Energy Agency details how hydropower can help ease the global addition of wind and solar to the resource mix on power grids. Argonne’s Audun Botterud offered his expertise as a co-author.