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|
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.
Applications will be subjected to scientific merit review (by the Screening Committee) and will be evaluated against the following criteria:
Relevance to the mission of the specific program (LLNL’s AI/ML relevant mission) to which the application is submitted.
- How does the proposed research contribute to the mission of the program in which the application is being evaluated?
- Is the proposed research aligned with the program office’s priorities as described in advisory committee reports?
Scientific and/or technical merit of the proposal.
- What is the scientific innovation of the proposed research?
- What is the likelihood of achieving high impact results?
- How might the results of the proposed work impact the direction, progress, and thinking in relevant scientific fields of research?
- Is the Data Management Plan suitable for the proposed research? To what extent does it support the validation of research results? To what extent will research products, including data, be made available and reusable to advance the field of research?
- How does the proposed work compare with other efforts in its field, both in terms of scientific and/or technical merit, scope, and originality?
LLNL engagement model, including proposal for onsite activities at LLNL and ability to pair the applicant with a researcher(s) at LLNL who can help oversee the applicant’s engagement at LLNL.
- How would the UC faculty and research group engage with LLNL?
- What is the plan to conduct onsite work at LLNL during the proposal period?
- What LLNL researcher(s) would the applicant propose to work with during the applicant’s activities at LLNL?
Appropriateness of the proposed method or approach.
- How logical and feasible are the research approaches?
- Does the proposed research employ innovative concepts or methods?
- Are the conceptual framework, methods, and analyses well justified, adequately developed, and likely to lead to scientifically valid conclusions?
- Does the applicant recognize significant potential problems and consider alternative strategies?
Competency of applicant’s personnel and adequacy of proposed resources.
- What is the past performance and potential of the PI?
- How well qualified is the research team to carry out the proposed research?
- Are the research environment and facilities adequate for performing the research?
- Does the proposed work take advantage of unique facilities and capabilities at LLNL?
Reasonableness and appropriateness of the proposed budget.
- Are the proposed budget and staffing levels adequate to carry out the proposed research?
- Is the budget reasonable and appropriate for the scope?
Potential for leadership within the scientific community and long-term technical and workforce benefit to LLNL.
- Scientific leadership can be defined very broadly and can include direct research contributions.
- How has the PI demonstrated the potential for scientific leadership and creative vision?
- How has the PI been recognized as a leader?
- How would this proposal benefit LLNL in the long-term, beyond the proposal period (including continuing technical engagement and potential workforce pipeline)?
The Selection Committee may consider any of the following program policy factors in making the selection, listed in no order of significance:
- Relevance of the proposed activity to LLNL priorities and technical merit and engagement with LLNL researchers
- Ensuring an appropriate balance of activities in collaboration between UC and LLNL programs
- Institutional history of training and mentoring early-career researchers
- Providing placement for postdoctoral researchers
- Mechanism for training the next generation of researchers
- Effective use of LLNL facilities
- Ensuring opportunities to investigators not currently supported by LLNL
- Commitment to sharing the results of research
- Promoting the diversity of supported investigators and institutions receiving the funding
LLNL and UC reserve the right to reject a proposal without review for the following reasons:
- The proposal is clearly nonresponsive to the objectives and/or provisions of the call for proposal.
- The proposal does not meet the requirements for proposal format, content, and organization as specified in the stated guidelines.
- The proposal is not submitted by the submission due date/time.
The recipient is requested to submit an annual report and conduct a program review at an agreed-upon time during the 5-year period. The renewal is appropriate if there are no changes in the following items:
- The recipient/applicant institution.
- The fundamental technical scope as proposed.
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.
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.
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.
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.
Templates are provided for both the expression of interest and proposal submission. Please use the form below to submit both.
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.
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.