Anuj J. Kapadia, who heads the Advanced Computing Methods for Health Sciences Section at ORNL, has been elected as president of the Southeastern Chapter of the American Association of Physicists in Medicine.
Chuck Greenfield, former assistant director of the DIII-D National Fusion Program at General Atomics, has joined ORNL as ITER R&D Lead.
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
Pablo Moriano, a research scientist in the Computer Science and Mathematics Division at ORNL, was selected as a member of the 2024 Class of MGB-SIAM Early Career Fellows.
Three staff members in ORNL’s Fusion and Fission Energy and Science Directorate have moved into newly established roles facilitating communication and program management with sponsors of the directorate’s Nuclear Energy and Fuel Cycle Division.
ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.
Scientists at ORNL are looking for a happy medium to enable the grid of the future, filling a gap between high and low voltages for power electronics technology that underpins the modern U.S. electric grid.
ORNL and the University of Tennessee, Knoxville, co-hosted the 2023 National Society of Black Physicists Annual Conference with the theme "Frontiers in Physics: From Quantum to Materials to the Cosmos.” As part of the three-day conference held near UT, attendees took a 30-mile trip to the ORNL campus for facility tours, science talks and workshops.
Gina Tourassi, associate laboratory director for computing and computational sciences at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory, has been named a fellow of the Institute of Electrical and Electronics Engineers, the world’s largest organization for technical professionals.
Researchers at the Department of Energy’s Oak Ridge and Lawrence Berkeley National Laboratories are evolving graph neural networks to scale on the nation’s most powerful computational resources, a necessary step in tackling today’s data-centric