Full-Time
Core Routing/IP Backbone Engineer
Lawrence Berkeley National Laboratory
5,001-10,000 employees
Fundamental science enabling energy and environment
Compensation Overview
$131.8k - $161.1k/yr
Berkeley, CA, USA
Hybrid
Bachelor's, Master's
 DevOps & Infrastructure (1) |
|---|
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- Typically requires a minimum of 5 years of related experience with a Bachelor’s degree; or 3 years and a Master’s degree; or equivalent work experience.
- Minimum of five years of relevant experience designing, deploying, operating, and troubleshooting network infrastructure and services.
- Demonstrated experience working with enterprise, service provider, research, or large-scale WAN/LAN environments.
- Demonstrated knowledge and hands-on experience with IP networking technologies and carrier-grade services including MPLS, QoS, traffic engineering, Layer 2/Layer 3 VPN technologies, and related service provider architectures.
- Experience configuring, troubleshooting, and operating advanced routing protocols and technologies, preferably including BGP, IS-IS, OSPF, Segment Routing, IPv4/IPv6, and MPLS within large-scale WAN, backbone, or service provider networking environments.
- Strong understanding of network troubleshooting methodologies with the ability to diagnose and resolve complex routing, transport, and end-to-end service issues across distributed wide-area infrastructures.
- Experience administering and operating multi-vendor routing platforms, preferably Juniper and Nokia (Alcatel-Lucent), in production backbone or carrier-class environments.
- Experience working in UNIX/Linux command-line environments, including remote systems administration, scripting fundamentals, and network automation workflows.
- Experience contributing to technical projects involving backbone deployments, network service implementations, migration planning, testing, operational readiness, and production support.
- Strong analytical, organizational, and problem-solving skills with the ability to manage operational and engineering issues of moderate to high complexity in large-scale network environments.
- Excellent written and verbal communication skills with the ability to collaborate effectively across engineering teams, operations groups, vendors, carriers, and scientific stakeholders.
- Demonstrated ability to work effectively in a highly collaborative, cross-functional technical environment supporting mission-critical network infrastructure.
- Experience using collaboration, ticketing, documentation, monitoring, and operational support tools within network engineering or service provider operations environments.
- Design, deploy, and support routing, switching, optical, and transport infrastructure across ESnet’s production and research networks.
- Configure and troubleshoot advanced networking technologies including IPv4/IPv6, BGP, IS-IS, MPLS, Segment Routing, RSVP, QoS, and L2/L3 VPNs.
- Support network operations, maintenance, upgrades, incident response, and service restoration.
- Develop automation tools and operational workflows to improve scalability, reliability, and efficiency.
- Collaborate with engineers, scientific stakeholders, vendors, carriers, and partner organizations to deliver and maintain network services.
- Participate in project planning, procurement, testing, deployment, and operational readiness activities.
- Create and maintain technical documentation, procedures, and network standards.
- Contribute to process improvements, troubleshoot moderate-complexity issues, and provide trusted technical guidance to stakeholders.
- Participate in an on-call rotation for business-hours and after-hours operational support.
- Operate with strong technical ownership and independence across infrastructure and operational initiatives.
- Lead projects, resolve complex technical issues, and contribute to network architecture and service design.
- Mentor junior engineers and drive operational improvements through automation, process optimization, and cross-team collaboration.
- Experience working in Research & Education (R&E), scientific computing, backbone, or service provider networking environments.
- Experience with network automation, model-driven telemetry, APIs, YANG/NETCONF, or infrastructure-as-code methodologies.
- Experience supporting optical transport systems, DWDM technologies, coherent optics, or long-haul backbone infrastructure.
- Familiarity with large-scale peering ecosystems, interdomain routing, and high-capacity backbone operations.
- Experience participating in on-call operational support rotations for production WAN or backbone network environments.
Berkeley Lab is a national research facility that conducts unclassified basic science across many fields, funded by the U.S. Department of Energy and managed by the University of California. Its work aims to address energy and environmental challenges by using interdisciplinary teams and building advanced tools for scientific discovery. Researchers study biosciences, computing sciences, Earth and environmental sciences, energy sciences and technologies, and physical sciences. The lab hosts about 4,200 scientists, engineers, staff, and students on a 200-acre site near UC Berkeley, and it has earned many prestigious honors, including Nobel Prizes and national academy memberships. Its goal is to generate foundational science that leads to practical, transformational solutions for energy and environmental issues while training the next generation of scientists and engineers.
Company Size
5,001-10,000
Company Stage
Grant
Total Funding
$2M
Headquarters
Berkeley, California
Founded
1931
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Simplify's Take
What believers are saying
- Doudna, NERSC-10, advances toward late-2026 deployment after March 17, 2026 system delivery.
- DOE's Genesis Mission picked Berkeley Lab collaborations with Texas A&M and eXoZymes in 2026.
- County-level Buildings Sector Scenarios data now guides utilities through 2050.
What critics are saying
- Berkeley Lab's first broad layoffs hit in 2025 after federal funding cuts.
- DOE's 2027 budget justification proposes a 20% Berkeley Lab cut, threatening research continuity.
- Dependence on DOE appropriations makes Doudna, NERSC, and workforce programs vulnerable if Congress stalls.
What makes Lawrence Berkeley National Laboratory unique
- NERSC at Berkeley Lab powers over 11,000 researchers and 800 annual projects.
- Berkeley Lab leads QSA, spanning trapped ions, superconductors, and neutral atoms since 2025 renewal.
- MOSAIC and PetaKit5D fuse microscopy, adaptive optics, and supercomputing for petabyte biology.
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↑ 3%Harpur College welcomes Simons Empire Faculty Fellows in mathematics and physics. The four assistant professors' research is at the intersection of quantum materials and artificial intelligence. Left to right: Simons Empire Faculty Fellows Lebing Chen, Sammy Luo, Yahong Yang, and Kunyan Zhang. Image Credit: Provided photos. By Jennifer Micale August 20, 2026 This semester, Harpur College of Arts and Sciences welcomes a quartet of mathematicians and physicists, who will help establish Binghamton University as a major research center at the intersection of quantum science and artificial intelligence. The assistant professors are among the first class of Simons Empire Faculty Fellows in the SUNY system. Mathematicians Yahong Yang and Sammy Luo bring expertise in deep learning theory for partial differential equations and discrete mathematics, with applications for machine learning and optimization. Meanwhile, physicists Lebing Chen and Kunyan Zhang bring cutting-edge experimental capabilities in quantum materials discovery and ultrafast spectroscopy of light-matter interactions. Altogether, their publication record includes prestigious journals such as Science Advances, Nature Communications, and Physical Review X, as well as premier machine-learning venues. The Simons Foundation and Simons Foundation International established the fellowship program to stimulate faculty hiring in mathematics and the sciences across New York state. Four tenure-track faculty members were hired at each of SUNY's University Centers, which include Binghamton, the State University of New York at Albany, Buffalo, and Stony Brook. Simons Foundation International provides funding to participating institutions, while the Simons Foundation administers the program. "I thank the Simons Foundation and Simons Foundation International for investing in SUNY's early-career faculty whose research shows promise in vital and emerging fields," said Binghamton University President Anne D'Alleva. "At Binghamton, our faculty are international leaders in topics including AI, quantum materials and sensing, and mathematics. The Simons Empire Faculty Fellows program provides essential support for their work as they make pathbreaking discoveries that shape our world." Earlier this year, Governor Kathy Hochul announced that the New York Center for AI Responsibility and Research, the first-ever independent artificial intelligence (AI) research center at any public university in the United States, will be established at Binghamton. The center will be used to develop technical tools that make AI safe to use in daily life and build upon the research of Binghamton faculty, who are using machine learning and data science to solve real-world challenges, from delivering better healthcare to improving information security. Previously a visiting assistant professor at the Georgia Institute of Technology, Yang investigates the mathematical foundations of AI, focusing on neural network approximation, generalization analysis, and symmetry-informed learning models. "I am very excited to join Binghamton University and honored to be a Simons Faculty Fellow. I was especially inspired by the University's vision for AI and interdisciplinary collaboration," Yang said. "My research focuses on using mathematics to better understand AI and to help develop AI methods for applications in other fields, so I am excited to connect with researchers across campus and develop new collaborations through this fellowship." Zhang comes to Binghamton from the Chemistry Department at the University of California, Berkeley. Her research focuses on exploring the fundamental physics of nanoscale quantum systems using ultrafast and multidimensional spectroscopy. By integrating advanced spectroscopic technologies, quantum materials, and machine learning, she aims to develop energy-efficient technologies and next-generation sensing platforms. "I am excited to develop my research in the vibrant community at Binghamton University, and I look forward to opportunities to connect and collaborate with other Simons Empire Faculty Fellows at the frontiers of AI and quantum science," Zhang said. Chen comes to Binghamton from a postdoctoral appointment at the University of California, Berkeley, and the Lawrence Berkeley National Laboratory. He is an experimental condensed matter physicist whose research centers on the design, synthesis, and spectroscopic study of quantum materials, which exhibit properties typically not observed in simple materials or at room temperature, such as superconductivity. Prior to Binghamton, Luo was a National Science Foundation Postdoctoral Fellow at the Massachusetts Institute of Technology. He works in combinatorics and graph theory on the discrete structures that underlie modern learning systems, including frameworks relevant to quantum circuits and error correction. All four are part of a quantum-AI research cluster. Chen anchors the materials side of the experimental program, to which Zhang provides the optical, ultrafast, and photonics components. Yang anchors the AI program in theory, while Luo provides the discrete mathematical layer that connects the experimental and theoretical sides. "We are grateful to the Simons Foundation for making it possible for us to recruit four fantastic scholars. The ability to hire a cluster of faculty who address overlapping questions through very different approaches and perspectives has enabled Harpur College to build research strength with both depth and breadth," said Harpur College Dean Celia Klin. "These scholars are exciting additions to the departments of Physics and Mathematics and will enhance the education of the students who take their courses and work with them on their research."
Berkeley Lab: building the computational mind for the 'swiss army knife' of microscopes. August 18, 2026 Press play to listen to this content Aug. 18, 2026 - Biology doesn't happen at one scale. Molecular interactions unfold in milliseconds and nanometers, while disease-associated change such as in Alzheimer's spreads across millimeters of brain tissue. Understanding complex biological systems requires scientists to watch both - ideally at the same time and with the same sample. Historically, this has meant shuttling samples between specialized instruments, often damaging biological context and slowing results. There's also a common crux across microscopes: the closer you look at living tissue, the more the image blurs, and the more detail you capture, the more overwhelming the resulting data becomes. Five imaging modes provide complementary views of the same dividing human retinal pigment epithelial cell (hTERT-RPE1). From top left, clockwise: Lattice Light-Sheet Structured Illumination Microscopy (LLS-SIM), widefield, 3D-SIM, oblique illumination, lattice light sheet. This shows how different microscopy techniques compare when imaging the same cell, giving them a more complete picture of what's happening inside. (Credit: Fu, Liu, Milkie, Ruan et al., Nature Methods, 2026) A new instrument aims to address these problems - and has revealed a third, arguably harder challenge that Berkeley Lab is uniquely positioned to address. Researchers at Lawrence Berkeley National Laboratory (Berkeley Lab) and collaborating institutions have developed the Multimodal Optical Scope with Adaptive Imaging Correction (MOSAIC) - a reconfigurable microscope that consolidates more than ten imaging techniques into one compact instrument. It processes its massive datasets using computational tools developed at Berkeley Lab, funded by a Laboratory Directed Research and Development (LDRD) award and supported by the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC). Featured on the cover of Nature Methods, MOSAIC allows scientists to track biological processes across scales and compare imaging methods on the same sample. It generates data at a pace that is pushing the boundaries of what biology can discover. "MOSAIC can generate up to four terabytes of data per hour - far beyond what conventional processing workflows or human inspection can handle," said Srigokul "Gokul" Upadhyayula, a faculty scientist in the Molecular Biophysics and Integrated Bioimaging Division and co-corresponding author. "The microscope is only as useful as our ability to process those data and extract biological meaning from them. Berkeley Lab's expertise in high-performance computing and large-scale data analysis is essential to closing that gap." How MOSAIC Works MOSAIC grew from the adaptive-optical lattice light-sheet microscope reported by Nobel laureate Eric Betzig, Upadhyayula, and their colleagues in 2018. That earlier system delivered exceptional performance but was so large it occupied a 10-foot by 4-foot optical table. As demand from the broader research community grew, the team designed the MOSAIC to retain and expand those capabilities while reducing the instrument's footprint. "MOSAIC has been built over a dozen times in different places with over 50 research licenses already shared. We also created comprehensive documentation on how to build this instrument - think an IKEA-style instruction set geared towards a scientist who has no deep optical expertise but is willing to learn." MOSAIC's main innovation is that it can be quickly reconfigured in two to five seconds to switch between a dozen distinct imaging modes. The team designed a smart modular system where the same lasers, mirrors, cameras, and computational hardware serve multiple imaging functions through a custom optical switching system. Critically, every one of those modes is enhanced with adaptive optics: a technology borrowed from astronomers who developed it to sharpen images of distant stars blurred by Earth's atmosphere. This corrects blurring caused by aberrations in the living tissue itself. "Sample-induced aberrations distort and redirect light, reducing both signal and resolution," said Upadhyayula. "Adaptive optics measures those distortions and corrects them. It is like turning on the windshield wipers while driving in the rain: the information is present all along, just obscured." MOSAIC also relies on fluorescent molecules that allow biologists to mark specific cellular structures and molecular activities in living cells, fast and gentle light-sheet imaging that captures cellular dynamics with minimal stress or damage, and high-speed data transfer infrastructure capable of moving and processing massive imaging datasets. What Becomes Visible When You Clear the Windshield? MOSAIC's ability to image with minimal invasiveness at large scales over long durations has already enabled several experiments: tracking single molecules in living cells, observing organelle dynamics in developing zebrafish embryos, mapping neuronal architecture in expanded human brain tissue from a person with Alzheimer's, and imaging neural activity in live mouse brains. In that last application, adaptive optics correction revealed roughly 2.5 times more detectable neural calcium events than imaging without it - suggesting conventional microscopy has been quietly undercounting brain activity. MOSAIC is also the instrument that powered a related study on Volumetric Imaging via Photochemical Sectioning (VIPS), published in Science in 2025. That project used MOSAIC and the computational tools developed at Berkeley Lab to image two complete adult mouse olfactory bulbs at nanoscale resolution, generating roughly a petabyte of data in approximately two weeks. Analyzing it took two years: an illustration of the gap between what these instruments can see and what researchers can currently process. "The bottleneck is no longer our ability to acquire the data," said Upadhyayula. "These microscopes can generate massive datasets at staggering rates. The key bottleneck is turning dense five-dimensional observations into biological understanding." Berkeley Lab's contribution helps to target this gap. Round-the-Clock Data Collection for Biological AI Supported by an LDRD award, Eric Betzig and Upadhyayula's group developed PetaKit5D, an open-source software toolkit that can handle MOSAIC's terabyte-per-hour output in real time and cuts processing costs by more than an order of magnitude compared to previous approaches. The team also secured computing allocations on the Perlmutter supercomputer at NERSC to process and visualize portions of the largest datasets. But processing data efficiently is only half the equation. The other half is generating enough of it - consistently, at scale, and of sufficient quality - to train the kind of AI model that could one day make sense of it all. At UC Berkeley, two MOSAIC instruments now run around the clock, capturing the five-dimensional data - three spatial dimensions, time, and molecular identity - that will be needed to train a new state-of-the-art AI model. The data flowing from those instruments already represents a fundamental shift in how biology can be practiced. For the first time, researchers can watch in vivo biochemistry unfold inside cells living within their native tissues, inside a living organism. What comes next, he believes, could be transformative: a vision language model that reasons natively over biology, connecting what it sees with molecular identity, experimental context, and prior biological knowledge to determine which observations matter and which experiments should come next. "Connected to automated microscopes, sample handling, and perturbation systems, that capability could provide the foundation for self-driving biological laboratories - and fundamentally change the rate at which we can make discoveries," said Upadhyayula. Deep Origin this month announced that its AI drug discovery framework delivered nearly a 31%... AI models are getting better at a rapid pace. They are now able to reason,... The tech industry is fixated on one main metric, the raw number of GPUs accumulated... Scientists running large simulations can end up with terabytes of data that then has to... Jensen Huang believes NVIDIA's chips are becoming much more than expensive pieces of hardware. As... As hybrid cognition deepens, the boundary between biological and artificial embodiment will begin to blur...
Predicting microelectronics performance with physics-informed artificial intelligence. Aug 06, 2026 A new AI framework will link atomic-scale defects to device performance, helping researchers detect failures earlier and design more reliable, efficient electronics. (Nanowerk News) How a microelectronic device performs depends on what it is made of, how well its materials and interfaces are built and how electricity, heat and tiny defects change over time. These devices power much of modern life - from smartphones and laptops to secure communications and artificial intelligence (AI) hardware. As next-generation devices become smaller, faster and more tightly packed, their performance is affected more and more by tiny flaws in materials and interfaces. These defects can lead to overheating, electrical leakage, unreliable switching and, in the end, shorter device lifetimes. But defects are not always harmful. They can also influence electrical and thermal behavior in useful ways, depending on how they are distributed and how they evolve over time. Understanding both the harmful and beneficial effects of defects is essential for designing better microelectronics. To address the challenges defects pose, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory, Lawrence Berkeley National Laboratory (Berkeley Lab), Oak Ridge National Laboratory (ORNL) and Northwestern University plan to develop the Materials Discovery Cloud. The project will create a physics-informed AI framework that learns how material composition, structure and operating conditions influence defect evolution and key functional properties such as electrostatic potential, current density and temperature. From protein folding to device function. AlphaFold is an AI system developed by Google DeepMind that predicts a protein's 3D structure based on its amino acid sequence. It transformed biology by making structure prediction much faster and more accurate, helping scientists better understand how proteins work. The new microelectronics effort follows a similar idea but applies it to a very different problem. Instead of predicting protein structure from sequence, the team aims to predict how networks of defects form, change and affect how a device works. "In biology, AlphaFold learned to connect sequence to structure," said Subramanian Sankaranarayanan, Argonne scientist and lead principal investigator on the project, as well as a professor at the University of Illinois Chicago. "We want to connect defect distributions in materials and interfaces to the electrical and thermal properties that matter for microelectronics. Such a framework remains elusive." Why defects are so hard to understand. Defects are tiny irregularities in a material's structure. They can include missing atoms, dislocations, voids or chemical disorder. Some defects hurt performance. Others can help enable useful behavior. The challenge is knowing which defects matter, when they matter and how they change under real operating conditions. These attributes are difficult to determine because no single instrument can capture the whole picture. Some tools, such as electron microscopes, can directly image features at very small scales. X-ray methods can reveal strain, buried structures and defect motion. Other techniques measure chemistry, electrical behavior and heat flow. Each method shows one part of the story, but not the whole system. A useful way to think about it is like trying to understand the day's weather from the temperature alone. Temperature tells you something important, but you also need to factor in wind, clouds, precipitation and humidity to see the full picture. The same is true for microelectronics. Bringing many tools into one framework. To close that gap, the team is combining many types of data from DOE Office of Science user facilities and advanced computing systems, drawing information from different tools and scales into one unified platform. At Argonne, those include the Advanced Photon Source and the Center for Nanoscale Materials for X-ray and microscopy measurements, as well as the Argonne Leadership Computing Facility for large-scale computing. Partner capabilities include the Advanced Light Source, the Molecular Foundry and the National Energy Research Scientific Computing Center at Berkeley Lab, and the Center for Nanophase Materials Sciences at ORNL. The Materials Discovery Cloud will help researchers gather experimental data, run simulations and generate synthetic data that mimic experiments. The synthetic data is especially important because complete experimental data sets for material samples are still complicated and time-consuming to collect. Simulations can fill in missing pieces and help train the AI framework. Another part of the effort focuses on autonomous discovery, which uses AI, machine learning and robotics to help researchers decide which measurements to run next and collect new data more quickly. That can help address one of the project's key challenges: generating enough high-quality experimental data to build and refine AI models. In this work, researchers are developing an AI-guided platform for microelectronics materials that can synthesize samples and carry out multiple kinds of characterization in a more integrated, high-throughput workflow. By reducing the need to move samples among facilities for separate measurements, the approach could speed data collection and help fill important gaps in the multimodal datasets used to train the framework. "What makes the Materials Discovery Cloud powerful is that we can bring together experiments, simulations and AI in one workflow," Sankaranarayanan explained. "That gives us a way to learn from limited data today while building a framework that can grow more capable as new data comes in." AI guided by physics. This system is not designed to be a black box that gives answers without explanation. Instead, it will be built around well-established laws of physics. This helps ensure the AI's predictions are grounded in how materials and devices actually behave. That certainty is important because the researchers want the system to do more than spot patterns in data. They want it to help reveal why certain defects lead to specific changes in performance, reliability or lifetime. To do that, the framework will combine several kinds of AI tools that can bring together many types of data, learn from both experiments and simulations and identify which new measurements would be most useful next. In the end, the goal is to connect tiny, atomic-scale features in a material to the larger electrical, thermal and mechanical behavior of a real device. What success could look like. If successful, the Materials Discovery Cloud could change how scientists design and test new materials and devices. Instead of waiting through long rounds of experiments, researchers may be able to get useful answers from a smaller set of early measurements. That could help them spot problems sooner, avoid spending time on weak candidates and focus more quickly on the most promising designs. Over time, the framework could also support inverse design. Rather than starting with a material and seeing how it performs, they could start with a goal - such as better heat management or a longer-lasting device - and ask what kind of material structure or defect pattern would be needed to achieve it. By bringing many different measurements into one predictive system, the effort aims to help scientists better understand, control and design the materials behind the next generation of microelectronics.
BCC and LBNL partner to expand quantum workforce readiness. Berkeley City College and Lawrence Berkeley National Laboratory (LBNL) have launched the Boosting Employment Readiness in Quantum (BERQ) program, a three-week summer initiative that introduces community college students to the rapidly advancing fields of quantum information science and technology while preparing them for careers in the emerging workforce. Running from July 27 through August 14, students will engage in a comprehensive curriculum that highlights current career opportunities in quantum technology. The daily schedule features expert-led lectures, hands-on laboratory work, and interactive career panels. In addition to technical training, students will participate in structured mentoring sessions dedicated to helping them create a clear, actionable roadmap for their future education and careers in science, technology, engineering, and mathematics. "Quantum is a rapidly emerging field critical to our nation's future and to maintain our technological leadership, we must cultivate a highly skilled workforce. Partnerships with academic institutions like Berkeley City College, help us achieve this goal. We look forward to providing an intensive dive into quantum information science and supporting students as they create a roadmap for their future in STEM," shared LBNL Director of STEM Education and Workforce Development Programs Faith Dukes. A major focus of the BERQ program is fostering meaningful connections between community college students, private industry innovators, and Department of Energy national lab research facilities. The program includes specialized site visits to Lawrence Livermore National Laboratory, the Berkeley based quantum computing company, Rigetti, and the University of California, Berkeley. Students will also gain industry perspectives through dedicated panels featuring representatives from prominent quantum technology partners, including IonQ and Quantum Machines. "Programs like BERQ demonstrate why community colleges play such a critical role in preparing the next generation of innovators by bridging the opportunity gap for students," said Berkeley City College Director of Workforce Development Ilona McGriff. "Through this partnership with Lawrence Berkeley National Laboratory, students can not only network and gain skills but see themselves as part of this rapidly growing industry." The technical components of the program are designed to provide practical, hands-on experience with modern quantum tools. This includes specialized technical workshops focused on systems like QubiC, alongside comprehensive educator support provided by Qolour. The summer initiative will culminate in a showcase event hosted at UC Berkeley, where participating students will deliver their final presentations to demonstrate their progress, technical knowledge, and career readiness to peers, educators, and industry professionals. The program is a workforce component of Berkeley Lab's Quantum Systems Accelerator, one of five DOE quantum research centers in the country. Funding for the program has been provided by the DOE Office of Science's Office of Workforce Development for Teachers and Scientists (WDTS).
DOE selects four Texas A&M AI projects for Genesis Mission. TL;DR - Key Takeaways * Texas A&M landed four projects in DOE's Genesis Mission, spanning critical minerals, particle accelerator operations and nuclear reactor safety. * Two teams will use multimodal AI to improve mineral exploration, combining geological, geochemical, microbial, hydrological and satellite data. * Agentic digital twins could help scientists maximize costly beam time by recommending equipment settings, monitoring sensors and detecting problems. Four Texas A&M research teams have been selected for the U.S. Department of Energy's Genesis Mission, where they will develop and test AI systems for critical mineral research, particle accelerator operations and nuclear reactor safety analysis. The projects are part of the Genesis Mission's recently announced initial research portfolio, drawn from what DOE called its largest response ever to a funding opportunity. This includes 278 projects involving 342 institutions, with universities leading 168 of them. The work is organized around 26 national science and technology challenges DOE identified in February, including fusion energy, quantum computing, chip design and advanced manufacturing. DOE said the teams will gain access to the initiative's shared platform of AI agent frameworks, advanced models, industry software and high performance computing resources at national laboratories and partner facilities. That infrastructure is central to the Genesis Mission's premise of connecting AI with the scientific data, supercomputers and research facilities scientists already use to help them move more quickly from experiments to results. DOE has set a goal of doubling the productivity and impact of U.S. research within a decade through faster data analysis, better use of scarce computing and laboratory resources, and greater coordination across research institutions. At Texas A&M, two of the four projects will apply multimodal AI to critical minerals. A team led by civil and environmental engineering professor Kung-Hui Chu will work with Lawrence Berkeley National Laboratory to combine microbial, hydrological and geochemical data to build AI tools for locating mineral deposits and studying biological methods of recovery. A second team, led by geology and geophysics professor Nicholas Perez, will analyze geological, geochemical, geophysical and satellite data to identify patterns associated with rare earth deposits. The researchers will study Texas, the Colorado Mineral Belt and other parts of the Southwest in collaboration with Pacific Northwest National Laboratory. Another project, led by Texas A&M assistant professor Jonas Karthein in collaboration with MIT's Laboratory for Nuclear Science, will test agentic digital twins at precision nuclear physics facilities, where data is limited and beam time can cost hundreds to more than $10,000 per hour. The system will learn how equipment behaves, recommend settings, monitor sensors and flag problems, helping researchers make better use of scarce experimental time. The team plans a nine-month proof of concept at Texas A&M and MIT, with possible expansion to larger facilities in a second phase. A fourth project will explore AI support for reactor safety and licensing. Headed by nuclear engineering professor Yang Liu, the SHIELD system will help run reactor models and simulations and prepare documents for regulatory review. The team will test it on a sodium-cooled reactor design and conventional large light-water reactors. DOE cautioned that the selections are subject to award negotiations and do not yet guarantee funding. But projects like these could give the Genesis Mission concrete measures of progress, including better mineral exploration decisions, more efficient use of beam time and faster preparation of reactor safety analyses. Their results could also help show where AI can make the greatest impact across scientific research.