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.
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Researchers at the Statewide California Earthquake Center are unraveling the mysteries of earthquakes by using physics-based computational models running on high-performance computing systems at ORNL.
EPB, ORNL announce plans for research collaborative focused on energy resilience, quantum technology
EPB and ORNL marked 10 years of collaboration with the announcement of the new Collaborative for Energy Resilience and Quantum Science.
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 l
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
From July 15 to 26, 2024, the Department of Energy’s Oak Ridge National Laboratory will host the second U.S. Quantum Information Science, or QIS, Summer School.
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
ORNL’s successes in QIS and its forward-looking strategy were recently recognized in the form of three funding awards that will help ensure the laboratory remains a leader in advancing quantum computers and networks.
In summer 2023, ORNL's Prasanna Balaprakash was invited to speak at a roundtable discussion focused on the importance of academic artificial intelligence research and development hosted by the White House Office of Science and Technology Policy and the
An innovative computational tool developed at Oak Ridge National Laboratory could reduce uncertainties and the time required to decide where to drill for gas and oil. Compared to previous approaches, this multilevel Monte Carlo method, which