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Media Contacts
ORNL has named Michael Parks director of the Computer Science and Mathematics Division within ORNL’s Computing and Computational Sciences Directorate. His hiring became effective March 13.
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.
ORNL’s next major computing achievement could open a new universe of scientific possibilities accelerated by the primal forces at the heart of matter and energy.
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.
ORNL researchers are deploying their broad expertise in climate data and modeling to create science-based mitigation strategies for cities stressed by climate change as part of two U.S. Department of Energy Urban Integrated Field Laboratory projects.
Doug Kothe has been named associate laboratory director for the Computing and Computational Sciences Directorate at ORNL, effective June 6.
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.
ORNL scientists will present new technologies available for licensing during the annual Technology Innovation Showcase. The event is 9 a.m. to 3 p.m. Thursday, June 16, at the Manufacturing Demonstration Facility at ORNL’s Hardin Valley campus.
It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.
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.