PNNL recently partnered with Amazon Web Services for AWS GameDay, a gamified learning event that challenges participants to use AWS solutions to solve real-world technical problems in a team-based setting.

New apps help researchers with statistical analyses of data.

Staff impressed the NNSA visitors with passion for their work and commitment to the national security mission.

PNNL is supporting the Department of Homeland Security Science and Technology Directorate's Chemical Security Analysis Center in improving capabilities to enhance detection and analysis of chemical threats.

The physics-informed machine learning optimization framework is now PNNL’s most popular open-source repository on GitHub.

PNNL researchers use chemometric analysis to model chemistry in molten salt.

In soil, microbes produce and consume methane. Using a technique called pool dilution, researchers can separate the rate of methane production and consumption from the net rate.

A special issue of the International Journal of Nuclear Security, in collaboration with the National Nuclear Security Administration Nuclear Security Women Initiative, focuses exclusively on gender in international nuclear security.

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.

PNNL researchers comment on the “Adversarial Machine Learning” white paper from the National Institute of Standards and Technology.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

PNNL has joined Gender Champions in Nuclear Policy, a leadership network that brings together leaders of organizations working in nuclear policy who are committed to breaking down gender barriers.

Researchers designed ultra-low radiation cables to reduce background noise for highly sensitive neutrino and dark matter detectors.

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

PNNL’s DataLife tool analyzes and identifies bottlenecks in scientific workflows.

Ripples demonstration will take place at the DOE booth at the International Conference for High Performance Computing, Networking, Storage, and Analysis.

A team of researchers from PNNL provided technical knowledge and support to test a suite of techniques that detect genetically modified bacteria, viruses, and cells.

Researchers from PNNL, CSET, and Google DeepMind delved into the potential risks of artificial intelligence in a roundtable discussion.

PNNL researchers helped design and conduct an international exercise hosted by the Ministry of Finance of Finland to help improve financial sector resilience.

PNNL’s Richard Saldanha will lead a new project to benchmark and validate models of cosmic ray exposure