Senior Semiconductor Laser Device Engineer

Lawrence Livermore National Laboratory (LLNL) and Oxylus Energy have secured $2 million in funding from the US Department of Energy's Industrial Technologies Office to develop reactor technology that converts waste carbon into methanol. The modular reactors transform carbon dioxide into valuable chemicals that can be used as fuel or precursors for plastics and other commodities. LLNL's novel electrochemical reactor converts carbon dioxide to carbon monoxide without alkali cations, which typically cause salt deposits that block reaction pathways. Oxylus Energy contributes technology for converting carbon monoxide to methanol. The partnership aims to integrate both systems to achieve direct carbon dioxide-to-methanol conversion in a single step. The technology could be deployed to existing plants and refineries, eliminating infrastructure costs whilst bolstering domestic manufacturing and energy security.

Connecting autonomous laboratories to speed scientific advancement. Media Inquiries Researchers at Carnegie Mellon University will develop AI-based tools that will enable autonomous laboratories to work together as one connected research ecosystem. The project has been selected to receive funding by the U.S. Department of Energy (DOE) as part of the Genesis Mission. The CMU research team, led by Herman Herman(opens in new window), director of CMU's National Robotics Engineering Center(opens in new window) (NREC), will partner with Argonne National Laboratory (ANL) and Lawrence Livermore National Laboratory (LLNL) to design and deploy a new generation of AI agents - intelligent software assistants that can make decisions and carry out tasks with minimal human guidance - to accelerate scientific breakthroughs by enabling robotic laboratories with different scientific equipment to work on complex experiments at the same time with minimal human involvement. Rather than requiring engineers to manually program each robot and scientific instrument, the AI agents will automatically generate the software instructions needed to perform complex tasks in each laboratory's unique environment. Using digital twins - virtual copies of each laboratory - the agents will practice, test, refine and optimize robotic workflows before deploying them to physical laboratory systems. "This project represents an important step toward intelligent laboratories that can learn, adapt, and collaborate," said Herman. "By combining AI agents, digital twins, and advanced robotics, we aim to create a scalable foundation for autonomous scientific discovery across Carnegie Mellon's AI Science Foundry and the national laboratory ecosystem." During the initial phase of the program, the team will demonstrate autonomous generation of robotic software instructions for increasingly complex laboratory tasks at CMU's AI Science Foundry(opens in new window) and testing at ANL and LLNL. The Genesis Mission is a historic national initiative led by the U.S. Department of Energy, which is building the world's most powerful integrated science discovery platform. By uniting government, industry, academia and philanthropy, it is accelerating breakthroughs in energy, scientific discovery and national security through a new platform that combines AI, supercomputing, quantum systems and advanced scientific instruments. The goal of the Phase I RFA awards is to identify promising pathways toward transformative scientific capabilities and establish a foundation for future investment and scale. Project teams will design and demonstrate research workflows that integrate AI with scientific investigation, while rigorously evaluating whether those approaches can accelerate discovery, improve predictive capabilities, enhance experimentation, or generate new scientific insights. Carnegie Mellon will lead the development of the AI-generated robotic autonomy stack and systems integration, while researchers at ANL, led by Arvind Ramanathan, a computational biologist in ANL's Data Science and Learning Division, will contribute expertise in data science and AI for biological research. Researchers at LLNL, led by Aldair Gongora, staff engineer in the Analytics for Advanced Manufacturing Group, and Yongqin Jiao, director of LLNL's Center for Bioengineering and Biomanufacturing, will advance autonomous experimentation platforms and synthetic biology capabilities that support distributed AI-driven scientific workflows. The project will use intelligent AI agents that can reason about laboratory capabilities, generate reusable robotic skills, adapt to differences in instruments and layouts, and continuously improve performance through simulation-based learning. By enabling laboratories to share robotic capabilities and AI-generated software modules, the platform is expected to dramatically reduce the time required to deploy new autonomous experiments across multiple sites. More stories. National Science Foundation research at CMU. National Science Foundation support helps Carnegie Mellon researchers across disciplines tackle some of society's most complex challenges. Explore the spotlights below to learn how CMU's NSF-funded research is making an impact.

University of Houston to lead DOE project using artificial intelligence to advance next-generation Energy Systems. DOE-funded collaboration combines artificial intelligence, advanced physics and engineering to accelerate the design of nuclear fission and fusion energy technologies July 22, 2026 - Key takeaways. * The University of Houston has been selected by the Department of Energy to lead a nearly $750,000 research project using artificial intelligence to accelerate the design of next-generation nuclear and fusion energy systems. * The interdisciplinary project combines AI, engineering and advanced physics to create faster, more accurate computer models that could help advance reliable, carbon-free energy technologies. * The award builds on UH's growing leadership in AI and energy innovation, bringing together world-class researchers, national laboratory partners and cutting-edge technologies to address some of the nation's most complex energy challenges. The University of Houston has been selected by the US. Department of Energy (DOE) to lead a nearly $750,000 collaborative research project that will harness AI to accelerate the design of next-generation nuclear and fusion energy systems. Funded through the DOE's Genesis Mission, the project brings together researchers from UH, Lawrence Livermore National Laboratory, and the University of Pennsylvania to develop AI-powered engineering tools capable of dramatically reducing the time needed to model complex heat-transfer systems critical to advanced nuclear reactors and future fusion technologies. By combining AI with the fundamental laws of physics, UH-led research aims to improve the accuracy and speed of computer models used to accelerate the development of reliable, carbon-free energy technologies. Where AI meets energy innovation. AI is rapidly transforming scientific discovery, and UH is applying its growing expertise to solve some of the most challenging problems in advanced energy systems. The project, led by Myoungkyu Lee, a professor at UH's Cullen College of Engineering, focuses on molten salts, whose unique characteristics make it uniquely suited for applications in energy production and storage technologies like nuclear reactors and hybrid energy systems. The goal for the project, titled "Physics-Informed AI Surrogates for Turbulent Forced Convection in Energy Systems," is to develop a faster, more reliable AI tool that predicts how heat moves through the turbulent liquid coolants used in advanced nuclear-fission reactors and future fusion systems. "Molten salts are attractive for several advanced reactor and fusion blanket concepts but are also among the hardest ones to model," Lee said. "Today's standard engineering tools were developed for fluids like water and air, and can significantly misjudge heat flow in molten salts. The most accurate simulations are too computationally expensive for everyday design work, so engineers currently bridge the gap with conservative safety margins. Our research aims to cut down the guesswork in an effort to cut conserve energy and cut costs." In collaboration with Lawrence Livermore National Laboratory and University of Pennsylvania, researchers will develop several highly detailed simulations to create training data, then teach an AI-assisted model to reproduce those results much faster. Unlike many AI systems, this model would still be required to follow the actual laws of fluid flow and heat transfer and would flag areas where its predictions may be unreliable. "The goal is to develop a tool that runs much faster than today's most detailed simulations while keeping errors small," Lee said. "If successful, the approach could support better heat-transfer predictions for molten-salt reactor design and provide a starting point for studying heat removal in fusion blankets. This is one contribution among many toward reliable, carbon-free energy." UH's continued leadership in research & innovation. The award adds to UH's growing portfolio of energy research, and the broader commitment to bringing together world-class researchers, cutting-edge technologies and collaborative partnerships with industry, government agencies and laboratories to advance AI applications across energy, engineering, manufacturing, and other critical industries. This latest award further strengthens UH's leadership in energy innovation. "Being part of this effort will continue to solidify UH's role as The Energy University," said Ramanan Krishnamoorti, Vice President for Energy and Innovation at UH. "" "This project combines some of UH's greatest assets - our leadership in energy and engineering, our world-class research, and our national partnerships," said Ramanan Krishnamoorti, Vice President for Energy and Innovation at UH. "Our faculty are at the forefront of discoveries and innovations that will change how we perceive energy systems for decades to come." By the numbers. * $180,000 awarded to UH * $380,000 awarded to LLNL * $190,000 awarded to UPenn