A multidisciplinary ORNL team used expertise in synthetic biology, AI-driven analysis, chemistry, neutrons and materials science to identify new members of a family of enzymes with a natural affinity for degrading synthetic nylon polymers.
In a major milestone for cancer research, the Department of Energy’s Oak Ridge National Laboratory helped reduce the time between cancer diagnosis and pathology report processing from 22 months to just 14 months, utilizing advanced artificial
Scientists conducted a groundbreaking study on the genetic data of over half a million U.S. veterans, using tools from the Oak Ridge National Laboratory to analyze 2,068 traits from the Million Veteran Program.
Researchers at Oak Ridge National Laboratory used the Frontier supercomputer to train the world’s largest AI model for weather prediction, paving the way for hyperlocal, ultra-accurate forecasts.
In partnership with the National Cancer Institute, researchers from the Department of Energy’s Oak Ridge National Laboratory’s Modeling Outcomes for Surveillance using Scalable Artificial Intelligence are building on their groundbreaking work to
Anuj J. Kapadia, who heads the Advanced Computing Methods for Health Sciences Section at ORNL, has been elected as president of the Southeastern Chapter of the American Association of Physicists in Medicine.
This summer, ORNL welcomed more than 500 students to campus through the lab’s range of internship programs, which are offered in areas such as biology, national security and computing.
Hilda Klasky, a research scientist in ORNL’s Computing and Computational Sciences Directorate, has been named a fellow of the American Medical Informatics Association.
Hilda Klasky, an R&D staff member in the Scalable Biomedical Modeling group at ORNL, has been selected as a senior member of the Association of Computing Machinery, or ACM.
Computational scientists and neutron structural biologists from Oak Ridge National Laboratory developed an integrated workflow using small-angle neutron scattering (SANS), atomistic molecular dynamics (MD) simulation, and an autoencoder-based deep learn