LLNL’s Early Career UC Faculty Initiative 2023

The Strategic Deterrence (SD) Directorate—formerly named Weapons and Complex Integration (WCI)—of Lawrence Livermore National Laboratory (LLNL) and the UC National Laboratories (UCNL) at the University of California (UC), Office of President (UCOP) are jointly inviting applications for the LLNL Early Career UC Faculty Initiative. The winning proposal is planned to commence funding in summer 2023 and is focused on supporting one untenured junior faculty in the UC system for this initiative cycle. The technical topic for this call is on artificial intelligence and machine learning (AI/ML), with specific details centering on overlapping interests between the UC faculty and LLNL’s programs and mission. The initiative is intended to develop next-generation UC academic leadership with strong and enduring LLNL and national laboratory connections. Please read on for requirements and submission guidelines.


February 10, 2023 Expression of interest submission deadline (see template and webform below)
March 10, 2023 Invitation to submit full proposals
April 5, 2023 Information Day at the UC Livermore Collaboration Center; learn more about SD's research in AI/ML, meet with technical PIs, and tour LLNL’s campus
May 6, 2023 Proposal submission deadline (see template and webform below)
July 2023 Recipient announcement

Requirements & FAQ

Duration and Funding Level: Initiative to fund untenured UC tenure-track faculty up to $1M over a 5-year period. The fund is structured to allow for faculty to build a research group, including undergraduates, graduate students, and postdoctoral fellows.

Eligibility: To be eligible for the initiative, a researcher must be an untenured, tenure-track faculty member at one of the ten University of California campuses. The recipient will be able to develop their innovative ideas and advance their research, with the goal leading to gaining tenure and internally and externally recognized professional leadership.


  • All work will be conducted at the Unclassified level.
  • Faculty should first submit an expression of interest describing their research interest and connection to SD’s technical focus areas (see next section). This allows LLNL’s technical PIs to be better prepared to engage UC faculty on preparing a proposal.
  • Applicants invited to submit a full proposal must work with LLNL technical staff members (identified by the Screening Committee) on the final submission package. This requirement provides LLNL researchers opportunities to collaborate and be more connected to the UC community to enhance its workforce and research objectives. The pre-proposal does not require identification of an LLNL PI, but must align with one of the technical topic areas.
  • Members of the UC research group must spend an agreed amount of time onsite at LLNL each year during the funded period. Dates, duration, and visiting members to be agreed upon by UC faculty recipient and LLNL collaborators.
    • The recipient and members of the UC research group will need to provide necessary information to LLNL Badge Office for badging and site access.
    • This required visit to LLNL is intended to strengthen the technical and workforce connections between UC and LLNL.
  • Expressions of interest and proposals must be submitted via the form at the bottom of this page.

Technical Focus Areas

The proposed research should focus on topics in the field of scientific AI/ML. Specifically, proposals should align with one or more of the following topic areas supported by this year’s initiative:

  • AI for advanced manufacturing. Digital twin development, AI process monitoring, control, and optimization and additive manufacturing.
  • AI-driven experiments. Development of methods for intelligent drivers and AI-driven facility operations, with particular focus on precise control, high-volume data, and high-repetition rate experiments.
  • Concept extraction from unstructured data. Autoclassification of historical and recent documents; natural language processing models to identify new findings from records.
  • Interpretable Bayesian analysis of high-dimensional data. Development of methods for combining simulations and measured spectral data to infer plasma conditions for complex high-energy-density science experiments.
  • Interpretable ML surrogates for time-dependent systems. Development of surrogate models to accelerate computationally demanding simulations for rapid decision making (e.g., optimization, inverse problems).
  • ML for linear induction accelerators. Detecting causes of instabilities, guide beam transport, automated analysis of system diagnostics.
  • ML-based radiographic analysis. Unsupervised ML for interpretable feature detection; similarity metrics for images, denoising of experimental data.
  • Object detection for autonomous drones. ML data fusion from multiple sensor modalities (e.g., chemical, optical, radio) for object detection; optimization of navigation during object search; one shot learning for autonomous drones.
  • Physics-aware ML for nuclear data. Leverage deep learning techniques to model reaction rates and decay properties of nuclides; propagate nuclear data uncertainties for applied science applications; identify highest priority experiments to reduce uncertainties.
  • Simulation-trained, observation-evaluated ML models. Detection of anomalous features between simulated and experimental images; developing statistical distances of experimental data from simulated distributions; projection of experimental data onto reduced order simulated data manifolds.
  • Strategies for sampling. Use AI/ML to guide the design of experiments in mid-high dimensional spaces. Develop provable measures of robustness, uncertainty, and convergence.

Selection Committee & Sponsors

Kim Budil, Director, LLNL

Brad Wallin, Deputy Director for Strategic Deterrence, LLNL

Pat Falcone, Deputy Laboratory Director for Science & Technology, LLNL

Craig Leasure, VP, UCNL, UCOP, Advisor to LLNL Selection Officials

LLNL logo

LLNL has a mission of strengthening the United States’ security through development and application of world-class science and technology to enhance the nation’s defense; reduce the global threat from terrorism and weapons of mass destruction; and respond with vision, quality, integrity, and technical excellence to scientific issues of national importance.

Strategic Deterrence logo


The SD (formerly WCI) organization provides foundational capabilities to a broad range of national security missions and ensures the success of the strategic nuclear deterrent into the future. Following the strong tradition of multidisciplinary team science, SD nurtures an exceptional workforce and effectively partner with stakeholders to achieve national security impact.

UCOP logo

UCNL plays a central role in providing leadership, management, and stewardship of the three UC affiliated national laboratories while informing The Regents and the UC President of national laboratory compliance and performance issues. The UCNL mission is to advance the research, education, and public service mission of the University of California by ensuring the long-term health and vitality of UC-affiliated national labs as centers of world-class science, technology, and innovation solving the world’s greatest challenges.

Submission Templates

Templates are provided for both the expression of interest and proposal submission. Please use the form below to submit both.

Expression of interest template (.docx, 262KB)

Proposal template (.docx, 266K)

Please note the following important elements of the proposal:

  • Diversity, equity, and inclusion: LLNL and UC recognizes and supports the benefits of having diverse and inclusive scientific, engineering, and technology communities and fully expects that such values will be reflected in the composition of all committees and proposal teams. LLNL and UC welcomes proposals in response to this call from all qualified and eligible UC faculty and Lab staff.
  • Developing the proposal: LLNL and UC provide no funding for reimbursement of proposal development costs. Technical and cost proposals (or any other material) submitted in response to this solicitation will not be returned. It is the policy of LLNL and UC to treat all proposals as competition-sensitive information and to disclose their contents only for the purpose of evaluation.
  • Person identifiable information (PII): Please do not include any PII in the proposal.
  • Conflict of interest (COI): Please list any potential COI from both LLNL and UC.
  • Export control: Majority of LLNL’s technical work are based on fundamental research that has no controls regarding broad academic collaborations. There are a limited technologies that may be subjected to a variety of economic and security controls. LLNL’s technical PI will work with UC faculty to avoid any potential export controls in the research proposal.

Contact & Submit

This webform accommodates any questions about this initiative and the submission process as well as submissions of expressions of interest (EOI) and proposals. Faculty who need travel support to attend the Information Days sessions in Livermore may also request assistance through this form by selecting the Question and/or information option. Refer also to the templates before submitting EOIs and proposals.