Data scientist at PNNL receives the Environmental and Engineering Geophysical Society and Geonics Limited Early Career Award for work with geophysical modeling and subsurface inversion codes.

The SHASTA program is doing a deep dive on subsurface hydrogen storage in underground caverns, helping to lay the foundation for a robust hydrogen economy.

The Deep Vadose Zone program leads a treatability study for subsurface remediation of the Hanford Site’s Central Plateau.

PNNL’s Jeff Burghardt, joins Sanford Underground Research Facility’s User Association Executive Committee.

PNNL’s Center for the Remediation of Complex Sites convened attendees from around the world to discuss challenges associated with environmental contamination.

Visual Sample Plan, a free software tool developed at PNNL that boosts statistics-based planning, has been recognized with a 2024 Federal Laboratory Consortium Award.

PNNL research unlocks key findings needed for potential commercial deployment of carbon mineralization technology.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

Tumeo will lead the ChemComp project, which was awarded for Exploratory Research in Extreme Scale Science

Stinis will serve as a principal investigator on two of the four projects selected for funding by the Advanced Scientific Computing Research program.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

PNNL chemists investigate the performance of ion exchange resins for subsurface remediation of uranium, technetium, and chromium.

David Barajas-Solano and Aaron Luttman worked with students to create models to evaluate remediation strategies at the Hanford Site.

The Rapid Assessment of Platform Technologies to Expedite Response project aims to prepare against future pandemics.

The award was given at the 29th International Conference on Information, Communication and Automation Technologies.

Zhijie (Jay) Xu will apply his expertise in multiscale modeling to this editorial role.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

ACMD staff contributed to 30 years of atmospheric data collection for the Department of Energy’s Atmospheric Radiation Measurement user facility.

Waste Management Symposia ‘Paper of Note’ and ‘Superior Paper’ awards recognize PNNL contributions to advancing radioactive waste and materials management.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.