Students often participate in internships and receive formal training in their chosen career fields during college, but some pursue professional development opportunities even earlier.
A modern, healthy transportation system is vital to the nation’s economic security and the American standard of living. The U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL) is engaged in a broad portfolio of scientific research for improved mobility
Artificial intelligence (AI) techniques have the potential to support medical decision-making, from diagnosing diseases to prescribing treatments. But to prioritize patient safety, researchers and practitioners must first ensure such methods are accurate.
Materials scientists, electrical engineers, computer scientists, and other members of the neuromorphic computing community from industry, academia, and government agencies gathered in downtown Knoxville July 23–25 to talk about what comes next in supercomputing after the end of Moore’s Law.
Six new nuclear reactor technologies are set to deploy for commercial use between 2030 and 2040. Called Generation IV nuclear reactors, they will operate with improved performance at dramatically higher temperatures than today’s reactors.
In the shifting landscape of global manufacturing, American ingenuity is once again giving U.S companies an edge with radical productivity improvements as a result of advanced materials and robotic systems developed at the Department of Energy’s Manufacturing Demonstration Facility (MDF) at Oak Ridge National Laboratory.
Scientists have demonstrated a new bio-inspired material for an eco-friendly and cost-effective approach to recovering uranium from seawater.
Researchers at the Department of Energy’s Oak Ridge National Laboratory, Pacific Northwest National Laboratory and Washington State University teamed up to investigate the complex dynamics of low-water liquids that challenge nuclear waste processing at federal cleanup sites.
Scientists at the Department of Energy’s Oak Ridge National Laboratory are working to understand both the complex nature of uranium and the various oxide forms it can take during processing steps that might occur throughout the nuclear fuel cycle.
Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.