U2opia Technology, a consortium of technology and administrative executives with extensive experience in both industry and defense, has exclusively licensed two technologies from ORNL that offer a new method for advanced cybersecurity monitoring in real time.
Three scientists from the Department of Energy’s Oak Ridge National Laboratory have been elected fellows of the American Association for the Advancement of Science, or AAAS.
A partnership of ORNL, the Tennessee Department of Economic and Community Development, the Community Reuse Organization of East Tennessee and TVA that aims to attract nuclear energy-related firms to Oak Ridge has been recognized with a state and local economic development award from the Federal Laboratory Consortium.
Having lived on three continents spanning the world’s four hemispheres, Philipe Ambrozio Dias understands the difficulties of moving to a new place.
Laboratory Director Thomas Zacharia presented five Director’s Awards during Saturday night's annual Awards Night event hosted by UT-Battelle, which manages ORNL for the Department of Energy.
Over the past seven years, researchers in ORNL’s Geospatial Science and Human Security Division have mapped and characterized all structures within the United States and its territories to aid FEMA in its response to disasters. This dataset provides a consistent, nationwide accounting of the buildings where people reside and work.
Larry Allard, a distinguished research staff member at Oak Ridge National Laboratory, has been named a Fellow of the Microanalysis Society.
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.
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.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.