Risk analysis on the plutonium-fueled power system that supplies electricity to the Mars rover answered the “what if” nuclear safety questions for NASA.
Marcel Baer is a computational scientist working in PNNL’s Physical Sciences Division with a prominent effort in materials science and physical bioscience.
High school students from across Washington State competed in the Pacific Northwest Regional Science Bowl, hosted online by PNNL, for a chance to advance to the national competition in May.
Johnson is among the PNNL scientists preparing to move into the Energy Sciences Center, the new $90 million, 140,000-square-foot facility that is expected to open in late 2021.
For the second straight year, PNNL researchers are featured in a special edition of the Journal of Information Warfare. This issue explores the topic of macro cyber resiliency.
PNNL highlights four researchers whose joint appointments are creating new and diverse opportunities for expanding knowledge and scientific impact across institutions.
PNNL scientists joined international leaders in artificial intelligence research to discuss the latest advances, opportunities, and challenges for neural information processing—the foundation for AI.
As COVID-19 was limiting in-person contact, halting travel, and creating additional barriers, researchers at PNNL were working to find solutions on how they could still get work done while establishing new safety protocols.
PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.
Researchers at PNNL have developed a bacteria testing system called OmniScreen that combines biological and synthetic chemistry with machine learning to hunt down pathogens before they strike.
PNNL’s new Smart Power Grid Simulator, or Smart-PGSim, combines high-performance computing and artificial intelligence to optimize power grid simulations without sacrificing accuracy.
PNNL researchers established an Internet of Things Common Operating Environment (IoTCOE) laboratory to explore the risks associated with IoT connectivity to the internet, the energy grid and other critical infrastructures.
PNNL researchers used machine learning to develop a tool for a nonprofit to identify orthopedic implants in X-ray images to improve surgical speed and accuracy.
PNNL team has developed and implemented a generalizable computational framework to study the resilience of the multilayered London Rail Network to the compound threat of intense flooding and a targeted cyberattack.