Filter News
Area of Research
News Topics
- (-) Artificial Intelligence (7)
- (-) Biomedical (3)
- (-) Environment (3)
- (-) Nanotechnology (3)
- Big Data (3)
- Bioenergy (2)
- Biology (3)
- Buildings (1)
- Climate Change (3)
- Computer Science (13)
- Coronavirus (2)
- Decarbonization (1)
- Exascale Computing (2)
- Frontier (3)
- High-Performance Computing (4)
- Machine Learning (2)
- Materials (4)
- Materials Science (3)
- Microscopy (1)
- National Security (1)
- Neutron Science (1)
- Nuclear Energy (1)
- Quantum Computing (5)
- Quantum Science (4)
- Simulation (3)
- Space Exploration (1)
- Summit (8)
- Sustainable Energy (1)
Media Contacts
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.
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.
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.
A study by researchers at the ORNL takes a fresh look at what could become the first step toward a new generation of solar batteries.
To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from ORNL developed a novel technique.
A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.
Students often participate in internships and receive formal training in their chosen career fields during college, but some pursue professional development opportunities even earlier.
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