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Media Contacts
Though Nell Barber wasn’t sure what her future held after graduating with a bachelor’s degree in psychology, she now uses her interest in human behavior to design systems that leverage machine learning algorithms to identify faces in a crowd.
ORNL researchers have developed an upcycling approach that adds value to discarded plastics for reuse in additive manufacturing, or 3D printing.
To optimize biomaterials for reliable, cost-effective paper production, building construction, and biofuel development, researchers often study the structure of plant cells using techniques such as freezing plant samples or placing them in a vacuum.
Two decades in the making, a new flagship facility for nuclear physics opened on May 2, and scientists from the Department of Energy’s Oak Ridge National Laboratory have a hand in 10 of its first 34 experiments.
The Frontier supercomputer at the Department of Energy’s Oak Ridge National Laboratory earned the top ranking today as the world’s fastest on the 59th TOP500 list, with 1.1 exaflops of performance. The system is the first to achieve an unprecedented level of computing performance known as exascale, a threshold of a quintillion calculations per second.
How an Alvin M. Weinberg Fellow is increasing security for critical infrastructure components
Jennifer Morrell-Falvey’s interest in visualizing the science behind natural processes was what drew her to ORNL in what she expected to be a short stint some 18 years ago.
Researchers at ORNL are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.
Scientists are using Oak Ridge National Laboratory’s Multicharged Ion Research Facility to simulate the cosmic origin of X-ray emissions resulting when highly charged ions collide with neutral atoms and molecules, such as helium and gaseous hydrogen.
An Oak Ridge National Laboratory team developed a novel technique using sensors to monitor seismic and acoustic activity and machine learning to differentiate operational activities at facilities from “noise” in the recorded data.