Please see the full solicitation for complete information about the funding opportunity. Below is a summary assembled by the Research & Innovation Office (RIO).
The DOE SC program in Nuclear Physics (NP) hereby announces its interest in receiving applications for research and development (R&D) efforts directed at artificial intelligence (AI) and machine learning (ML) for autonomous optimization and control of accelerators and detectors of relevance to current or next generation NP accelerator facilities and scientific instrumentation, as well as applications applying AI/ML to advance nuclear physics computations.
Current and planned NP facilities and scientific instrumentation face a variety of technical challenges in theory, simulations, control, data acquisition, and data analysis. AI methods and techniques promise to address these challenges and shorten the timeline for experimental and computational discovery.
The approach for this NOFO is to support the development and application of AI/ML in all research areas of NP to expand and accelerate scientific reach and discovery. Opportunities include AI to address challenges in autonomous control, efficiency of operation of accelerators and scientific instruments, digital twinning for future colliders, efficient extraction of critical information from large complex data sets and enabling data-driven discovery of new physics. Major areas of research may include, for example:
- Efficient extraction of critical and strategic information from large complex data sets:
- Development and implementation of digital twins for future colliders;
- Efforts to address the challenges of autonomous control and experimentation,
- Efficient operation of accelerators and scientific instruments,
- Deployment of AI for reduction of large and/or complex experimental data,
- Development of software to enable data-driven discovery of new physics
See the solicitation for full details.
Deadlines
CU Internal Deadline: 11:59pm MST November 3, 2024
DOE Letter of Intent Deadline: 3:00pm MST November 14, 2024
DOE Application Deadline: 3:00pm MST December 5, 2024
Internal Application Requirements (all in PDF format)
- Letter of Intent (3 pages maximum): Include a list of all senior/key personnel at the applicant and team member institutions. This information must be followed by a clear and concise description of the objectives and technical approach of the proposed research. The description of the proposed research may not exceed two pages, when printed using standard letter-size (8.5-inch x 11-inch) paper with 1-inch margins (top, bottom, left, and right). The body text font must not be smaller than 11-point. Figures and references, if included, must fit within the two-page limit. If a multi-institutional team is being proposed, list all institutions by name with each institution鈥檚 PI.
- Lead PI Curriculum Vitae
- Budget Overview (1 page maximum): A basic budget outlining project costs is sufficient; detailed OCG budgets are not required. Details should be included on the source(s) of cost-share.
To access the online application, visit:
Eligibility
The PI on a letter of intent, or application may also be listed as a senior or key personnel, including in any role on a proposed subaward, on an unlimited number of separate submissions. Lead PIs are expected to budget at least 20% of their time to the applications on which they are named.
Limited Submission Guidelines
Applicant institutions are limited to no more than three letters of intent per institution as the lead applicant of a multi-institutional team or as the sole applicant. There is no limitation to the number of applications on which an institution appears as a subrecipient for which the institution is not the lead.
Award Information
Individual awards may vary between $200K - $3.5M
Number of Awards: 10-15
Period of Performance: 2 years听
Review Criteria
The internal committee will use DOE鈥檚 evaluation criteria (below) for the selection process.
- Scientific and/or Technical Merit of the Project
- What is the scientific innovation of the proposed research?
- What is the likelihood of achieving valuable results?
- How might the results of the proposed work impact the direction, progress, and thinking in relevant scientific fields of research?
- How does the proposed work compare with other efforts in its field, both in terms of scientific and/or technical merit and originality?
- 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?
- For renewal applications only: Is the proposed work an appropriate outgrowth of, continuation to, or successor of the currently supported research?
- 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?
- Does the proposed plan to recruit and retain students and early-stage investigators provide sufficient mentorship?
- Competency of Applicant鈥檚 Personnel and Adequacy of Proposed Resources
- For renewal applications, what is the past performance and potential of the research team?
- How well qualified is the research team to carry out the proposed research?
- Is the lead institution proposing to perform a greater portion of the scientific and technical work than any other team member?
- Are the research environment and facilities adequate for performing the research?
- Does the proposed work take advantage of unique facilities and capabilities?
- 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?
- Quality and Efficacy of the Promoting Inclusive and Equitable Research Plan
- How well integrated is the Promoting Inclusive and Equitable Research (PIER) Plan with the proposed project?
- What aspects of the PIER plan are likely to contribute to the goal of creating and maintaining an equitable, inclusive, encouraging, and professional training and research environment and supporting a sense of belonging among project personnel?
- How does the proposed plan include intentional mentorship of project personnel?
- How are the proposed resources and budget for the PIER Plan reasonable and appropriate?
- To what extent is the PIER plan likely to lead to participation of individuals from diverse backgrounds, including individuals historically underrepresented in the research community?
- Relevance to compelling scientific opportunities identified in the 2023 NSAC Long Range Plan
- Does the proposed research advance the scientific opportunities identified in the 2023 NSAC Long Range Pan ( )
- Can the proposed research be exploited to advance discoveries?