A trio of new and improved cosmological simulation codes was unveiled in a series of presentations at the annual April Meeting of the American Physical Society in Minneapolis.
The Autonomous Systems group at ORNL is in high demand as it incorporates remote sensing into projects needing a bird’s-eye perspective.
Environmental scientists at ORNL have recently expanded collaborations with minority-serving institutions and historically Black colleges and universities across the nation to broaden the experiences and skills of student scientists while bringing fresh insights to the national lab’s missions.
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.
Having lived on three continents spanning the world’s four hemispheres, Philipe Ambrozio Dias understands the difficulties of moving to a new place.
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.
In human security research, Thomaz Carvalhaes says, there are typically two perspectives: technocentric and human centric. Rather than pick just one for his work, Carvalhaes uses data from both perspectives to understand how technology impacts the lives of people.
A new paper published in Nature Communications adds further evidence to the bradykinin storm theory of COVID-19’s viral pathogenesis — a theory that was posited two years ago by a team of researchers at the Department of Energy’s Oak Ridge National Laboratory.
Scientists develop environmental justice lens to identify neighborhoods vulnerable to climate change
A new capability to identify urban neighborhoods, down to the block and building level, that are most vulnerable to climate change could help ensure that mitigation and resilience programs reach the people who need them the most.
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.