Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.
A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.
Hailong Wang is a non-federal co-lead for the Arctic Systems Interactions Collaboration Team that will explore the Arctic’s dynamic interconnected systems.
Gosline works to develop computational algorithms that are uniquely targeted for rare disease work by doing foundational research in model system development. This work can be expanded to all model systems in human disease.
Cesar Moriel from University of Texas at El Paso will be interning at the PNNL over the summer as part of the Energy Environment Diversity Internship Program.
A scenario approach was used to explore the potential future role of hydropower around the globe considering the multisectoral dynamics of regional energy systems and basin-specific water resources.
Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.
Report for the Oregon Public Utility Commission highlights innovations and best practices for resilience and utility planning could be helpful to other states as well.
