Senior Machine Learning Engineer

PNNL, SEPA jointly provide new interactive map showing state-by-state wildfire legislation. Toward enabling states to see how other states are tackling wildfire risks to the grid, the U.S. Department of Energy's Pacific Northwest National Laboratory (PNNL) and the Smart Electric Power Alliance (SEPA) have teamed up on a new interactive map that provides a nationwide view of state-by-state wildfire legislation. The Wildfire Regulation and Policy Tracker (WRAPT) shows what topics are prioritized over time, which states are pursuing which wildfire mitigation strategies, which measures passed or failed, and where an agency has stepped in to create a comparable statewide requirement. "Wildfire risks for utilities were once a Western issue but are now a national issue and moving fast," said Rebecca O'Neil, advisor for electricity infrastructure at PNNL. "States across the country can benefit from an interactive tool like WRAPT - from states in the West as they consider policy options and interactions, states in the Midwest as they consider best practices and get in front of this issue as it arrives, and states in the East adapting this playbook as they balance utility investment against the threat of wildfire." Research shows that in 2025, states considered more wildfire-related legislation than they had during the previous 10 years combined. While PNNL had already been tracking legislation data in a much simpler dataset over the last year, SEPA last spring launched its own review of state legislation for its Wildfire Technology Roadmap. After a chance encounter with the PNNL team at a conference and comparing notes, SEPA saw an opportunity to transform PNNL's dataset into an interactive map. WRAPT shows users a broader, more accessible view of state-by-state wildfire legislation. Currently, the map shows 44 legislative and regulatory actions across 18 states. PNNL and SEPA plan to update the map as new actions are introduced. "SEPA has spent years helping the industry make sense of the policy shifts reshaping the grid, and wildfire governance is now one of the fastest-moving fronts," said Jared Leader, SEPA's senior director of Resilience and Global Initiatives. "WRAPT reflects the kind of work SEPA is built for: pairing PNNL's research with our sector perspective to translate policy developments across states into something decision-makers can act on." WRAPT also offers an alternative for those involved in wildfire mitigation planning who would otherwise have to search through hundreds of documents cast in state-specific contexts buried within bill management systems in state legislative sessions, O'Neil said. With WRAPT, users can select a state and quickly see what approaches it has considered. The new map also helps states tackle practical questions: What have other states tried? Which bills became law? Which proposals failed? What issues are regulators addressing? What lessons can be applied directly state-to-state and what would need to be adapted? "The partnership with SEPA is helping move wildfire risk research beyond the laboratory and into the hands of people making decisions," said O'Neil. "The more that state officials and regulators can understand why different states are making different choices, the easier it is for them to interpret and make the right choice for themselves."

PNNL, Fervo and NVIDIA build AI digital twin to run geothermal reservoirs in real time. A federal laboratory, a geothermal developer and an AI computing company announced on June 22, 2026, that they are jointly building an AI-powered virtual replica of an enhanced geothermal reservoir, a tool its creators say could eliminate weeks-long analysis waits that today leave underground power plants operating below their potential. What EGS-Twin is and how it works. Fervo Energy, NVIDIA and Pacific Northwest National Laboratory (PNNL) announced an agreement to develop a next-generation digital twin platform for Enhanced Geothermal Systems (EGS) technology, known as EGS-Twin. EGS-Twin is designed to deliver real-time insight into subsurface behavior and operational performance through the integration of high-resolution field data with physics-based modeling and AI-driven forecasting. Fervo Energy deploys fiber-optic cables and uses acoustic technology to map and gather intelligence from the subsurface, but processing and analyzing all that data takes too much time for operators to act swiftly. Fluctuations in the operating data can indicate issues in wells, reservoirs or pipelines that may require attention. Current models that help represent the dynamics of a geothermal system can take weeks to run. EGS-Twin would run in real time and allow operators to respond quickly to any underground problems that may arise. To build EGS-Twin, PNNL researchers will use Fervo's industry expertise and field data to train scalable AI models on NVIDIA AI infrastructure. The trained AI models will then be integrated into the NVIDIA Omniverse libraries, helping geothermal operators more quickly identify and respond to subsurface changes, optimize power generation, and strengthen the scalability of enhanced geothermal systems. Data sources and the road to commercial deployment. Using currently available proprietary field data from Fervo's Nevada and Utah sites, the PNNL team will begin training the digital twin immediately and continue refining the platform as additional production data comes online, with the platform scheduled for implementation by 2029. The final EGS-Twin will contain anonymized data so that other geothermal plant operators can adapt it to their own operations. That open-access design matters in an emerging industry where a broader, standardized modeling tool could compress the learning curve for every developer that follows. Fervo's CTO and co-founder Jack Norbeck said: "We believe that digital twins will expedite the learning curve for geothermal development as we build and operate our GeoBlock assets." He added that "integrating high-fidelity physics-based models with AI-driven forecasting has the potential to reshape reservoir management, improve heat recovery, and enhance system reliability." Fervo's Cape Station context. Fervo established Project Red in Nevada in 2023, which now generates 3 megawatts for the grid. Cape Station Phase I will deliver 100 megawatts (MW) of baseload clean power to the grid beginning in 2026, and Cape Station Phase II will bring an additional 400 MW online by 2028. Fervo is building its Cape Station plant in Beaver County, Utah, which is expected to begin delivering electricity to the grid later this year and will ultimately generate 500 megawatts. Cape Station is expected to be the largest next-generation geothermal development in the world. Geothermal made up just 0.4% of all electricity generated in the United States in 2023. But according to the U.S. Department of Energy, geothermal could provide potentially 90 gigawatts of firm and flexible power to America's grid by 2050, assuming that enhanced systems like Fervo's catch on as a widespread renewable energy option. Getting there will depend partly on whether operators can extract more from each plant they drill, which is precisely the problem EGS-Twin targets. Why real-time reservoir intelligence matters now. The EGS-Twin partnership reflects a wider convergence of AI infrastructure and baseload power ambitions. As GCN has reported, only 13 percent of US power projects in the grid interconnection queue reach commercial operation, a failure rate that presses every geothermal developer to demonstrate that operating plants can perform reliably once they are running. Poor real-time visibility into reservoir conditions is one of the operational risks that can knock capacity factors well below the round-the-clock figures written into power contracts. Maruti Mudunuru, the PNNL Earth scientist leading the project as principal investigator, said that "current modeling capabilities for geothermal systems are too slow to fully incorporate and analyze production data, which can lead to an underutilized resource," and that a working digital twin would allow EGS operators to "understand, in real time, the dynamics of their reservoir and act quickly to maximize the power generation potential." The team will use currently available results to begin training the digital twin and will continue developing it as additional production data comes online. The platform is expected to be up and running by 2029. The development also connects directly to the broader push to make geothermal a viable baseload option for data-center power demand. Federal support for EGS has expanded on multiple fronts this year, from Pennsylvania shale conversions to multi-state consortia, and EGS-Twin represents one of the first efforts to apply large-scale AI infrastructure to the operational side of that buildout. Hugo Rojas is the editor of GCN. With a Master of Science in Engineering, he specializes in technology, data, and science, and brings a human-centered perspective informed by psychology.

Oceans contain billions of tonnes of uranium. The attraction of seawater uranium is the sheer size of the potential resource. Known uranium resources on land are estimated at around 7.9 million tonnes, while the world's oceans are estimated to contain approximately 4.5 billion tonnes of uranium. Much of it entered the oceans over geological timescales as uranium was weathered from rocks and carried into the sea. For countries expanding nuclear power, that could represent a potentially enormous additional source of nuclear fuel. China is particularly dependent on uranium imports. According to the World Nuclear Association figures cited by South China Morning Post (SCMP), Chinese mines produced about 1,600 tonnes of uranium in 2024, compared with roughly 13,000 tonnes required by its nuclear reactors. The US has spent decades investigating whether seawater could help close this gap. Researchers at Oak Ridge National Laboratory and Pacific Northwest National Laboratory developed and tested uranium-absorbing fibers in natural seawater, including experiments at Washington's Sequim Bay. The US program ultimately found that the economics remained unfavorable compared with conventional land-based mining. PhosCage has so far only been demonstrated under controlled laboratory conditions. The Qingdao researchers collected seawater off the coast of Qingdao and processed 25 liters through a laboratory flow system rather than deploying the material directly in the ocean. Large-scale ocean deployment would introduce challenges involving currents, waves, fouling by marine organisms, material durability, recovery of the adsorbent, and processing costs. The researchers also did not provide a cost estimate for the uranium recovered in their experiments. They say their next steps include scaling up the material and reducing the overall cost of extracting uranium from seawater. Get the latest in engineering, tech, space & science - delivered daily to your inbox. You may unsubscribe at any time. Kaif Shaikh is a journalist and writer passionate about turning complex information into clear, impactful stories. His writing covers technology, sustainability, geopolitics, and occasionally fiction. A graduate in Journalism and Mass Communication, his work has appeared in the Times of India and beyond. After a near-fatal experience, Kaif began seeing both stories and silences differently. Outside work, he juggles far too many projects and passions, but always makes time to read, reflect, and hold onto the thread of wonder.