Research Initiatives and Infrastructure

Research and Innovation
You are here: Home / Archives for Current Limited Submissions

Current Limited Submissions

PAR-24-128: Medical Scientist Training Program (MSTP) (T32)

Slots: 1

Deadlines

Internal Deadline: Friday, June 27th, 2025, 5pm PT Contact RII.

LOI: N/A

External Deadline: September 25, 2025

Recurring Deadlines: January 25, 2026; May 25, 2026; September 25, 2026; January 25, 2027

Award Information

Award Type: Grant

Estimated Number of Awards: The number of awards is contingent upon NIH appropriations and the number of meritorious applications submitted.

Anticipated Award Amount: Application budgets are not limited but must reflect the actual needs of the proposed project.  

Who May Serve as PI: 

  • To provide research training leadership for the program, at least one of the training PDs/PIs should have a record of using rigorous and transparent methods in experimental design, data collection, analysis, and reporting in a biomedical research field applicable to the program.
  • Additional PDs/PIs may be included to strengthen the expertise of the PD/PI team. Examples include individuals such as program directors who regularly interact with students, or individuals with expertise in education, relevant social sciences, program evaluation, mentoring, or university administration. 

Any of the PDs/PIs may serve as the contact PD/PI. The contact PD/PI is expected to have a full-time appointment at the applicant organization unless extremely well-justified. If the full-time status of the contact PD/PI changes after the award, the organization must obtain prior program approval to appoint a new PD/PI or request a deviation from the full-time rule. The PD(s)/PI(s) will be responsible for:

  • The overall direction, management, administration, and evaluation of the program.
  • The day-to-day administration of the program, including direct involvement with trainees.
  • The selection and appointment of trainees to the research training program.
  • The selection of faculty mentors for the program, assessment of mentor performance, and ensuring the program deals appropriately with substandard mentor performance.
  • Monitoring and assessing the program and submitting all documents and reports as required.
  • Appointing members of the Advisory Committee (when applicable) and implementing their guidance as appropriate.

Link to Award: https://grants.nih.gov/grants/guide/pa-files/PAR-24-128.html

Process for Limited Submissions

PIs must submit their application as a Limited Submission through the Research Initiatives and Infrastructure (RII) Application Portal: https://rii.usc.edu/oor-portal/. Use the template provided here: RII Limited Submission Applicant Template

Materials to submit include:

  • (1) Two-Page Proposal Summary (1” margins; single-spaced; standard font type, e.g. Arial, Helvetica, Times New Roman, or Georgia typeface; font size: 11 pt). Page limit includes references and illustrations. Pages that exceed the 2-page limit will be excluded from review. You must use the template linked above.
  • (2) CV – (5 pages maximum)

Note: The portal requires information about the PIs in addition to department and contact information, including the 10-digit USC ID#, Gender, and Ethnicity. Please have this material prepared before beginning this application.

Purpose

The overall goal of the NIH Ruth L. Kirschstein National Research Service Award (NRSA) program is to help ensure that a diverse pool of highly trained scientists is available in appropriate scientific disciplines to address the nation’s biomedical, behavioral, and clinical research needs. More information about NRSA programs may be found at the Ruth L. Kirschstein National Research Service Award website. The NRSA program has been the primary means of supporting predoctoral and postdoctoral research training programs since enactment of the NRSA legislation in 1974. 

Each NIGMS-funded MSTP award is expected to provide a rigorous, well-designed research training program that includes mentored research experiences, courses, seminars, and additional training opportunities that equip clinician scientists with the following skills required for careers in the biomedical research workforce:

  • Technical (for example, appropriate methods, technologies, and quantitative/computational approaches).
  • Operational (for example, independent knowledge acquisition, rigorous experimental design, interpretation of data, and conducting research in the safest manner possible).
  • Professional (for example, management, leadership, communication, and teamwork).

Through this funding announcement, NIGMS encourages changes in integrated clinical and graduate research training to keep pace with the rapid evolution of the biomedical research enterprise, which is increasingly complex, interdisciplinary, quantitative, and collaborative. Other changes in the biomedical research enterprise include greater diversity in the backgrounds of people participating in biomedical research, the approaches utilized to investigate clinically relevant research questions, and the range of careers that dual-degree recipients are pursuing. Additionally, there is an increasing recognition of the need to enhance reproducibility of biomedical research results through scientific rigor and transparency, and to promote a culture where the highest standards of practice are used to ensure the safety of all individuals in the research environment. This funding opportunity is intended to encourage and enable the scientific community to develop and implement evidence-informed approaches to biomedical research training and mentoring that will effectively train future generations of rigorous clinician scientists to become leaders in biomedical research and clinical medicine.

Programs are encouraged not to simply layer additional activities onto existing structures but to instead use creative and transformational approaches to integrate clinical and biomedical graduate training, including curricular reform, that preserve the best elements of current programs, while enhancing the focus on the development of trainee skills.

NIGMS strives to ensure that future generations of researchers will be drawn from the entire pool of potential contributors and seeks to expand opportunities to support individuals from a variety of backgrounds at multiple training and career stages in a variety of organizations and educational settings across the country. The Overarching Objective of the MSTP is to develop a diverse pool of well-trained clinician scientists (that is, a Ph.D. combined with a clinical degree, such as an M.D., D.O., D.V.M., D.D.S., Pharm.D., etc.) who have the following:

  • A broad understanding across biomedical disciplines.
  • The skills to independently acquire the knowledge needed to advance their chosen fields and careers.
  • The ability to think critically and identify important biomedical research questions and approaches that push forward the boundaries of their areas of study.
  • A strong foundation in scientific reasoning, rigorous research design, experimental methods, quantitative and computational approaches, and data analysis and interpretation.
  • The skills to conduct research in the safest manner possible, and a commitment to approaching and conducting biomedical research responsibly, ethically, and with integrity.
  • Experience initiating, conducting, interpreting, and presenting rigorous and reproducible biomedical research with increasing self-direction.
  • The ability to utilize clinical experience and observations to identify biomedical research questions and to develop impactful research programs that push forward the boundaries of their areas of study.
  • The skills necessary to integrate research and clinical activities and the capacity to translate scientific research findings into clinical practice.
  • The ability and skills to lead changes that promote health equity, reduce health disparities and improve the health of those medically underserved across diseases, disorders, and conditions.
  • The ability to work effectively in teams with colleagues from a variety of cultural and scientific backgrounds, and to promote inclusive and supportive scientific research environments.
  • The skills to teach and communicate scientific methodologies and findings to a wide variety of audiences (for example, discipline-specific, across disciplines, and for the public).
  • The knowledge, professional skills and experiences required to identify and transition into careers in the biomedical research workforce that utilize the dual-degrees (for example, the breadth of careers that sustain biomedical research in areas that are relevant to the NIH mission).

Program Considerations

NIGMS will accept predoctoral training grant applications supporting integrated clinician and graduate research training through this MSTP funding announcement and subsequent reissuances (graduate research training in basic biomedical sciences is supported through PAR-23-228 and subsequent reissuances). Applicants are strongly encouraged to read information about NIGMS predoctoral training grant programs, including the MSTP and Leading Equity and Diversity in the Medical Scientist Training Program (LEAD MSTP) (PAR-23-030), found on the NIGMS website and to contact program staff before preparing or submitting an application  to verify that the proposed program is eligible and in alignment with NIGMS funding priorities.

General Considerations. NIGMS intends to fund applications that propose feasible, rigorous, well-designed and integrated dual-degree research training programs that will build on the most effective elements of successful programs, while encouraging creative and transformational approaches to clinician scientist research training, ranging from curricular reform to changes in the research training environment. Funded programs should implement plans to optimize the time required to earn the dual degree. Programs are expected to limit appointments to individuals committed to research careers that utilize the dual-degree qualifications.

NIGMS encourages programs to devise and test alternative entry pathways in addition to or instead of the direct application and admission to the first year of the dual-degree training program, thus providing opportunities to recruit students from clinician-only or from Ph.D.-only programs.  NIGMS encourages institutions to offer MSTP trainees the opportunity to earn the Ph.D. in a broad range of biomedical, physical, and social and behavioral sciences, and engineering to meet the needs for clinician scientist researchers in all areas of the biomedical workforce.  Programs that provide interdisciplinary research training, incorporate training in data science, or take advantage of clinical research opportunities within nationwide networks and infrastructures such as the NIH Clinical and Translational Science Award program are encouraged to apply.  NIGMS encourages applicants to offer training across the landscape of medical fields and scientific disciplines related to health, and to promote opportunities for the exploration of clinician scientist career options. Funded programs are expected to:

  • Be a well integrated dual-degree program  that exerts a strong, positive influence at the organizational level on research training and mentoring practices.
  • Have clearly defined training objectives and show evidence of meeting the objectives in progress reports and in renewal applications.
  • Implement evidence-informed training and mentoring activities (for example, approaches that are grounded in the literature and evaluations of existing relevant dual-degree research training programs). Programs are expected to be responsive to evaluations, particularly with respect to trainee feedback.
  • Provide rigorous, well-designed mentored research experiences, and additional opportunities that will build a strong cohort of dual-degree research-oriented individuals. Training grant funds may not be used solely as a vehicle to provide financial aid for trainees to conduct research.
  • Demonstrate effective oversight of dual-degree trainee development and promote retention for the entire time the dual-degree trainee is in the training program. Retention efforts are activities designed to sustain the scientific interests and participation of trainees from all backgrounds. Retention and oversight activities might include monitoring academic and research progress, building strong trainee cohorts, as well as increasing science identity, self-efficacy, and a sense of belonging within research training environments. Programs are expected to make efforts to identify individuals who may need additional academic and social supports to successfully complete the program, and ensure they receive the needed support.
  • Promote inclusive, safe, accessible, and supportive research training environments to maximize success for all individuals in the training program. Specifically, funded programs should have organizational and departmental environments where individuals from all backgrounds are welcomed, feel integrated into, and supported by the biomedical research community. Safety in research training should encompass (1) environments free from harassment, discrimination, and intimidation, in which all are treated in a respectful and supportive manner, (2) laboratory and clinical settings where individuals exercise the highest standards of practice for chemical, biological and physical safety, and (3) practices at the organizational leadership and research community levels that demonstrate core values and behaviors to emphasize safety over competing goals.

Trainee Support. The training grant defrays the cost of stipends, tuition and fees, and training related expenses, including health insurance, for the appointed trainees in accordance with the approved NIH NRSA support levels.   NIGMS typically provides full-time support for approximately 25% of the trainees in the training program during any given year.  Individuals may receive up to six years of aggregate Kirschstein-NRSA support at the predoctoral level for dual-degree training, including any combination of support from institutional training grants (for example, T32) and an individual fellowship award (for example, F30 or F31 awards).  Many dual-degree training programs guarantee enrolled students full support for the duration of the dual-degree program (through combinations of federal support, institutional funds, other fellowships, and grants). Training programs may implement institutional policies regarding payback of non-NRSA institutional sources of funds by students who start training but do not complete one or both degrees.  NIGMS does not require nor permit institutions to receive payback NRSA funds from trainees who are appointed to NIH training grants, but do not complete training.

Synergies of Federally Funded Training Programs. Funded research training programs are encouraged to complement and synergize with other ongoing federally supported predoctoral research training programs at the applicant organization (for example, in the development of skills needed for careers in the biomedical research workforce that are not discipline-specific); however, the scientific training goals must be distinct from related programs at the same organization currently receiving federal support. In cases where an organization has multiple NIGMS predoctoral training grants, it is expected that these programs will work together to create administrative and training efficiencies to reduce costs and improve trainee services and outcomes.

Visit our Institutionally Limited Submission webpage for more updates and other announcements.

LifeArc Rare Disease Clinical Trials Programme

Slots: 1

Deadlines

Internal Deadline: Friday, June 20th, 2025, 5pm PT Contact RII.

LOI: N/A

External Deadline: Proposals will be assessed and progressed on a case-by-case basis. How long the programme stays open will depend on demand. Early engagement highly recommended. 

Award Information

Award Type: Grant

Estimated Number of Awards: 

Anticipated Award Amount: Applicants can request up to £5 million per proposal.

Who May Submit: Applications are open to not-for-profit institutions (including academic institutes, healthcare organisations, public sector research establishments etc) and SMEs, either in the UK or abroad.  

Link to Award:https://www.lifearc.org/project/rare-disease-clinical-trials-programme/

Process for Limited Submissions

PIs must submit their application as a Limited Submission through the Research Initiatives and Infrastructure (RII) Application Portal: https://rii.usc.edu/oor-portal/. Use the template provided here: RII Limited Submission Applicant Template

Materials to submit include:

  • (1) Two-Page Proposal Summary (1” margins; single-spaced; standard font type, e.g. Arial, Helvetica, Times New Roman, or Georgia typeface; font size: 11 pt). Page limit includes references and illustrations. Pages that exceed the 2-page limit will be excluded from review. You must use the template linked above.
  • (2) CV – (5 pages maximum)

Note: The portal requires information about the PIs in addition to department and contact information, including the 10-digit USC ID#. Please have this material prepared before beginning this application.

Purpose

About the programme

This programme is an exciting opportunity for researchers working on any rare disease to apply for funding, alongside advice and support from our internal teams, to progress their innovations through clinical trials.

We are looking to fund early-stage clinical trials for rare diseases that will deliver high-quality efficacy and safety data, with a view to driving patient impact within the next 3-5 years.

If you believe you have a suitable project, early engagement is encouraged. You can contact us via the form below to learn more and enquire about submitting an expression of interest.

Proposals should:

  • adress an unmet rare disease medical need.
  • be underpinned with a strong scientific rationale.
  • be well developed with a clear clinical trial plan.
  • have consideration of the future route to patient impact within 5 years.
  • have an established manufacturing process for GMP trial supplies (or equivalent for non-therapeutic interventions).
  • include prior or planned patient and public involvement and engagement.
  • include objectives to develop/validate biomarkers and enable targeted treatment.
  • include a clear plan on the approach to access and recruitment of the rare disease population.

Proposals can include:

  • co-funding with other partners.
  • trials centred in the UK or abroad.

What is not in scope?

Proposals that focus solely on non-clinical development or manufacture will not be considered.

Proposals focused on running clinical studies and not clinical trials, such as clinical studies focused only on biomarker discovery / validation or projects focused on natural history studies or equivalent types of clinical studies, will not be considered.

Proposals that are not sufficiently developed for rapid progression to clinical development (e.g. do not have sufficient non-clinical evidence, manufacturing or clinical trial plans in place) will not be progressed.

Visit our Institutionally Limited Submission webpage for more updates and other announcements.

2025-NIST-CHIPS-NAPMP-01: National Advanced Packaging Manufacturing Program (NAPMP) Advanced Packaging Research and Development (R&D)

Slots: Eligible applicants may submit only one concept paper per R&D Area.

Deadlines

Internal Deadline: Friday, November 15th, 2024, 5pm PT Contact RII.

Concept Paper Deadline (required): December 20, 2024

External Deadline (invited):  Full applications will be due 60 days from the date of the invitation to submit.

Award Information

Award Type: Other (research and development)

Estimated Number of Awards: 10

Anticipated Award Amount: CHIPS R&D anticipates making available up to approximately $1,550,000,000 for funding multiple awards of varying size and scope, with anticipated amounts ranging from approximately $10,000,000 to approximately $150,000,000 in Federal funds per award over a five (5) year period of performance.

Who May Serve as PI: Eligible applicants are domestic non-profit organizations; domestic accredited institutions of higher education; State, local, and Tribal governments; and domestic for-profit organizations.

Link to Award: https://www.grants.gov/search-results-detail/356762

Process for Limited Submissions

PIs must submit their application as a Limited Submission through the Research Initiatives and Infrastructure (RII) Application Portal: https://rii.usc.edu/oor-portal/. Use the template provided here: RII Limited Submission Applicant Template

Materials to submit include:

  • (1) Two-Page Proposal Summary (1” margins; single-spaced; standard font type, e.g. Arial, Helvetica, Times New Roman, or Georgia typeface; font size: 11 pt). Page limit includes references and illustrations. Pages that exceed the 2-page limit will be excluded from review. You must use the template linked above. Please note which R&D Area you are applying to.
  • (2) CV – (5 pages maximum)

Note: The portal requires information about the PIs in addition to department and contact information, including the 10-digit USC ID#. Please have this material prepared before beginning this application.

Purpose

This NOFO envisions projects in five (5) R&D Areas: 

(1) Equipment, Tools, Processes, and Process Integration;

(2) Power Delivery and Thermal Management; 

(3) Connector Technology, including Photonics and RF; 

(4) Chiplets Ecosystem; and 

(5) Co-design/EDA.

The 2022 CHIPS and Science Act appropriated $50 billion to the U.S. Department of Commerce (the Department’s) CHIPS for America program, to support semiconductor research and development (R&D) and to expand semiconductor manufacturing capacity in the United States. This includes $39 billion for the Department to expand domestic semiconductor manufacturing capacity through an incentives program and $11 billion to advance U.S. leadership in semiconductor R&D. These R&D advances are being realized through four programs: (1) the National Semiconductor Technology Center (NSTC), (2) the NAPMP, (3) the CHIPS Metrology Program, and (4) a CHIPS Manufacturing USA Institute. These investments, across both the R&D and incentives programs, seek to strengthen U.S. competitiveness, support domestic production and innovation, create, across the country, good jobs with working conditions consistent with the Good Jobs Principles published by the Departments of Commerce and Labor, and advance U.S. economic and national security

Visit our Institutionally Limited Submission webpage for more updates and other announcements.

NSF 24-599: Quantum Leap Challenge Institutes (QLCI)

Slots: one taken, one still available.

Deadlines

Internal Deadline: Friday, November 1st, 2024, 5pm PT. Contact RII as one slot is still available.

LOI: (required) February 7, 2025

Preliminary Proposal Deadline: March 7, 2025

External Deadline: (by invitation only) September 17, 2025

Award Information

Award Type: Cooperative Agreement

Estimated Number of Awards: 5 to 10

Anticipated Award Amount:  $200,000,000 to $300,000,000

Who May Serve as PI: No restrictions or limits.

Link to Award: https://new.nsf.gov/funding/opportunities/quantum-leap-challenge-institutes-qlci/nsf24-599/solicitation

Process for Limited Submissions

PIs must submit their application as a Limited Submission through the Research Initiatives and Infrastructure (RII) Application Portal: https://rii.usc.edu/oor-portal/. Use the template provided here: RII Limited Submission Applicant Template

Materials to submit include:

  • (1) Two-Page Proposal Summary (1” margins; single-spaced; standard font type, e.g. Arial, Helvetica, Times New Roman, or Georgia typeface; font size: 11 pt). Page limit includes references and illustrations. Pages that exceed the 2-page limit will be excluded from review. You must use the template linked above.
  • (2) CV – (5 pages maximum)

Note: The portal requires information about the PIs in addition to department and contact information, including the 10-digit USC ID#. Please have this material prepared before beginning this application.

Purpose

Quantum Leap Challenge Institutes are large-scale interdisciplinary research projects motivated by major challenges at the frontiers of quantum information science and technology (QIST). Institutes are expected to catalyze breakthroughs on important problems underpinning QIST, for example in the focus areas of quantum computation, quantum communication, quantum simulation and/or quantum sensing. Successful institutes will coordinate a variety of approaches to specific scientific, technological, and educational goals in these fields, including multiple institutions and building upon multiple disciplines, as motivated by the science and engineering challenges. In so doing, Institutes will nurture a culture of discovery, provide education, training, and workforce development opportunities in the context of cutting-edge research, and demonstrate value-added from synergistic coordination within the institute and with the broader community. Partnerships, infrastructure, industry engagement, outreach, international collaboration, and new applications for QIST should be fostered by Institutes in support of their research, education, and coordination goals.

The QLCI program can support awards to continue existing Quantum Leap Challenge Institutes or to establish and operate new Quantum Leap Challenge Institutes. In either case, proposers should follow the same guidance for Challenge Institute proposal preparation described in this solicitation. While this is a crosscutting program, proposals responding to this solicitation must be submitted to the Office of Strategic Initiatives (OSI) in the Directorate of Mathematical and Physical Sciences (MPS). They will subsequently be managed by a cross-disciplinary team of NSF Program Directors.

The QLCI program enables NSF multidisciplinary centers for quantum research and education as called for in the National Quantum Initiative (NQI) Act1 and an NQI Advisory Committee report, Renewing the National Quantum Initiative: Recommendations for Sustaining American Leadership in Quantum Information Science2.In alignment with the NQI Act, Quantum Leap Challenge Institutes shall pursue research at the frontiers of quantum information science, engineering, and technology, and explore solutions to important challenges for the development, application, commercialization, and pioneering use of quantum technologies. QLCI Institutes shall also lead education, training, and workforce development activities as may be needed for sustained leadership in QIST and related topics. Coordination both within each Institute and with new partners and the broader ecosystem should also serve to galvanize the community and catalyze the research and education activities in ways that go beyond what smaller projects could accomplish in isolation.

Visit our Institutionally Limited Submission webpage for more updates and other announcements.

NSF 20-554: ADVANCE: Organizational Change for Gender Equity in STEM Academic Professions (ADVANCE)

Slots: 1 total. Eligible IHEs can submit one proposal to IT-Preliminary, Adaptation, OR Catalyst.

Deadlines

Internal Deadline: Friday, March 1st, 2024 Contact RII.

LOI: Adaptation and Partnership only: August 5, 2024; First Monday in August, Annually Thereafter

Preliminary Proposal:  IT-Preliminary: April 25, 2024; Fourth Thursday in April, Annually Thereafter. IT-preliminary proposals are accepted before and after the target date.

External Deadline:

Adaptation and Partnership: November 6, 2024; First Wednesday in November, Annually Thereafter

Institutional Transformation: October 3, 2024; First Thursday in October, Annually Thereafter

Catalyst: August 2, 2024; First Friday in August, Annually Thereafter. Catalyst proposals are accepted before and after the target date. Please contact the program office before submitting a proposal to discuss timing for submission.

Award Information

Award Type: Standard Grant or Continuing Grant or Cooperative Agreement

Estimated Number of Awards: 18 – 36

Anticipated Amount: The total number of awards to be made under this solicitation is estimated to be between 18 and 36 over two fiscal years.

In each year, NSF expects to make approximately:

  • six Adaptation awards up to $1,000,000 for three-year long projects
  • six Partnership awards up to $1,000,000 for up to five-year long projects
  • four Catalyst awards up to $300K for two years
  • Additionally, in FY 2021, the program anticipates making up to two Institutional Transformation awards for up to $3,000,000 for five-years.

Who May Serve as PI: No restrictions or limits.

Link to Award: https://www.nsf.gov/pubs/2020/nsf20554/nsf20554.htm

Process for Limited Submissions

PIs must submit their application as a Limited Submission through the Research Initiatives and Infrastructure (RII) Application Portal: https://rii.usc.edu/oor-portal/. Use the template provided here: RII Limited Submission Applicant Template

Materials to submit include:

  • (1) Two-Page Proposal Summary (1” margins; single-spaced; standard font type, e.g. Arial, Helvetica, Times New Roman, or Georgia typeface; font size: 11 pt). Page limit includes references and illustrations. Pages that exceed the 2-page limit will be excluded from review. You must use the template linked above.
  • (2) CV – (5 pages maximum)

Note: The portal requires information about the PIs in addition to department and contact information, including the 10-digit USC ID#, Gender, and Ethnicity. Please have this material prepared before beginning this application.

Purpose

The NSF ADVANCE program goal is to broaden the implementation of evidence-based systemic change strategies that promote equity for STEM2 faculty in academic workplaces and the academic profession. The NSF ADVANCE program provides grants to enhance the systemic factors that support equity and inclusion and to mitigate the systemic factors that create inequities in the academic profession and workplaces. Systemic (or organizational) inequities may exist in areas such as policy and practice as well as in organizational culture and climate. For example, practices in academic departments that result in the inequitable allocation of service or teaching assignments may impede research productivity, delay advancement, and create a culture of differential treatment and rewards. Similarly, policies and procedures that do not mitigate implicit bias in hiring, tenure, and promotion decisions could lead to women and racial and ethnic minorities being evaluated less favorably, perpetuating historical under-participation in STEM academic careers and contributing to an academic climate that is not inclusive.

All NSF ADVANCE proposals are expected to use intersectional approaches in the design of systemic change strategies in recognition that gender, race and ethnicity do not exist in isolation from each other and from other categories of social identity. The solicitation includes four funding tracks: Institutional Transformation (IT), Adaptation, Partnership, and Catalyst, in support of the NSF ADVANCE program goal to broaden the implementation of systemic strategies that promote equity for STEM faculty in academic workplaces and the academic profession. For more information on each category, see the link above.

The Institutional Transformation (IT) track is designed to support the development, implementation, and evaluation of innovative systemic change strategies that promote gender equity for STEM faculty within an institution of higher education.

The Adaptation track is designed to support the work to adapt, implement, and evaluate evidence-based systemic change strategies that have been shown to promote gender equity for STEM faculty in academic workplaces and the academic profession. Adaptation projects can either: 1) support the adaptation of evidence-based systemic change strategies to promote equity for STEM faculty within an institution of higher education; or 2) facilitate national or regional STEM disciplinary transformation by adapting evidence-based systemic change strategies to non-profit, non-academic organizations.

The Partnership track is designed to support the work to facilitate the broader adaptation of gender equity and systemic change strategies. Partnership projects are expected to result in national or regional transformation in STEM academic workplaces and the academic profession and demonstrate significant reach. Partnership projects can focus on the transformation of institutions and organizations and/or the transformation within one or more STEM disciplines.

The Catalyst track is designed to broaden the types of IHEs that are able to undertake data collection and institutional self-assessment work to identify systemic gender inequities impacting their STEM faculty so that these can be addressed by the institution.

Budgetary Requirements: Inclusion of voluntary committed cost sharing is prohibited.

NSF-23-610: National Artificial Intelligence (AI) Research Institutes Accelerating Research, Transforming Society, and Growing the American Workforce

Slots: 2. An organization may submit no more than two preliminary proposals to this solicitation as lead institution. This limit is solicitation-wide and applies across the groups and themes. An organization may submit up to two full proposals that correspond to preliminary proposals reviewed under this solicitation. In the event that an organization exceeds these limits, preliminary proposals will be accepted based on earliest date and time of preliminary proposal submission, i.e., the first two preliminary proposals will be accepted, and the remainder will be returned without review. A full proposal that does not correspond to a preliminary proposal reviewed in this program will be returned without review.

Deadlines

Internal Deadline: Friday, September 15th, 2023 for all groups and themes. Contact RII.

LOI: October 31, 2023 for themes listed Under Group 1; January 12, 2024 for themes listed under Group 2

External Deadline: February 16, 2024 for Themes listed under Group 1; May 17, 2024 for themes listed under Group 2.

Award Information

Award Type: Cooperative Agreement

Estimated Number of Awards: 5

Anticipated Award Amount: Institute awards will be made for between $16,000,000 and $20,000,000 for four to five years ($4,000,000 per year on average)

Who May Serve as PI: There are no restrictions or limits.

Link to Award: https://www.nsf.gov/pubs/2023/nsf23610/nsf23610.htm

Process for Limited Submissions

PIs must submit their application as a Limited Submission through the Research Initiatives and Infrastructure (RII) Application Portal: https://rii.usc.edu/oor-portal/. Use the template provided here: RII Limited Submission Applicant Template

Materials to submit include:

  • (1) Two-Page Proposal Summary (1” margins; single-spaced; standard font type, e.g. Arial, Helvetica, Times New Roman, or Georgia typeface; font size: 11 pt). Page limit includes references and illustrations. Pages that exceed the 2-page limit will be excluded from review. You must use the template linked above.
  • (2) CV – (5 pages maximum)

Note: The portal requires information about the PIs in addition to department and contact information, including the 10-digit USC ID#, Gender, and Ethnicity. Please have this material prepared before beginning this application.

Purpose

Artificial Intelligence (AI) has advanced tremendously and today promises personalized healthcare; enhanced national security; improved transportation; and more effective education, to name just a few benefits. Increased computing power, the availability of large datasets and streaming data, and algorithmic advances in machine learning (ML) have made it possible for AI research and development to create new sectors of the economy and revitalize industries. Continued advancement, enabled by sustained federal investment and channeled toward issues of national importance, holds the potential for further economic impact and quality-of-life improvements.

The 2023 update to the National Artificial Intelligence Research and Development Strategic Plan, informed by visioning activities in the scientific community as well as interaction with the public, identifies as its first strategic objective the need to make long-term investments in AI research in areas with the potential for long-term payoffs in AI. AI Institutes represent a cornerstone Federal Government commitment to fostering long-term, fundamental research in AI while also delivering significantly on each of the other eight objectives in that strategy. The National Security Commission on Artificial Intelligence (NSCAI) identifies AI Institutes as a key component of a bold, sustained federal push to scale and coordinate federal AI R&D funding and to reinforce the foundation of technical leadership in AI.

This program is a multisector effort led by the National Science Foundation (NSF), in partnership with the Simons Foundation (SF), the National Institute of Standards and Technology (NIST), Department of Defense (DOD) Office of the Under Secretary of Defense for Research and Engineering (OUSD (R&E)), Capital One Financial Corporation (Capital One), and Intel Corporation (Intel).

This program solicitation expands the nationwide network of AI Research Institutes with new funding opportunities over the next two years. In this round, the program invites proposals for institutes that have a principal focus in one of the following themes aimed at transformational advances in a range of economic sectors, and science and engineering fields:

  • Group 1 – Awards anticipated in FY 2024:
    • Theme 1: AI for Astronomical Sciences
  • Group 2 – Awards anticipated in FY 2025:
    • Theme 2: AI for Discovery in Materials Research
    • Theme 3: Strengthening AI

For the institute themes listed in Group 1, NSF anticipates awards to start in FY 2024; and for themes listed in Group 2, NSF anticipates awards to start in FY 2025. Each group has a specific set of due dates and review timeline pertaining only to that group. More detail is found under Due Dates and in the timeline provided in the Program Description.

II.A. AI Research Institutes Scope

The vision of the National AI Research Institutes program is broad and ambitious. It is expected that each AI Research Institute will pursue this vision in ways that are uniquely suited to its selected research focus, facilities, collaborations, and other unique circumstances. Proposers are encouraged to convey the unique qualities of the proposed Institute, while addressing the following desiderata common to all AI Research Institutes proposed to this program:

  • AI Research Institutes advance foundational AI research that will have broad and lasting impact, contributing new knowledge or methods toward understanding of the mechanisms underlying thought and intelligent behavior and their implementation in machines (see the definition of AI specified above). Institutes aimed at advancing established AI lines of research should demonstrate the potential to radically advance these areas beyond the state of the art. Institutes might also address new foundational AI research priorities that arise from rapid advances in AI and the increasing ubiquity of AI-enabled technology. Institute proposals that do not describe a clear plan to achieve ambitious advances in foundational AI research are not likely to be responsive to this solicitation.
  • AI Research Institutes conduct use-inspired research that both informs foundational AI advances and drives innovations in related sectors of science and engineering, segments of the economy, or societal needs. Effective use-inspired research achieves synergy among a group of researchers to enable transformative advances in AI, related sectors, and the interfaces between these areas. This dimension of an AI Research Institute will feature clear and compelling goals to advance AI and to accelerate the fielding of AI-powered innovation; it also enhances the transfer of knowledge through the meaningful exchange of scientific and technical information with external stakeholders such as industrial partners, public policy makers, or international organizations, as well as with the broader scientific and educational community. Through use-inspired research, Institutes have the potential to create and share new community infrastructure, including data and software, to further research, promote reproducibility, and support education.
    It is critical that proposals clearly specify how the use-inspired context for Institute research reveals the opportunities for foundational AI advances and how those foundational AI advances in turn contribute to the related sectors that define the use-inspired context.
  • AI Research Institutes actively build the next generation of talent for a diverse, well-trained workforce. Specifically, AI Research Institutes should leverage the visionary nature of their research foci to drive new and innovative education and development tailored toward, e.g., undergraduates, graduate students, and post-doctoral researchers, as well as through community colleges and skilled technical workforce training and other opportunities as appropriate that advance knowledge and education of AI, including public understanding of AI. This could include innovative pedagogy and instructional materials, advanced learning technologies, project-driven training, cross-disciplinary and collaborative research, industry partnerships, and new career pathways. Institutes should offer broad, deep, and diverse experiences to build the next generation of the AI workforce, with a focus on broadening participation among the full range of groups currently under-represented in science and engineering. AI Research Institutes should maximize their unique position to grow the next generation of talent that will provide new discoveries and leadership.
  • AI Research Institutes are coherent multidisciplinary groups of scientists, engineers and educators appropriate for a large-scale, long-term research agenda for the advancement of AI and the fielding of AI-powered innovation in application sectors of national importance. The multidisciplinary nature of these Institutes will catalyze foresight and adaptability beyond what is possible in single research projects; further, the individual projects that an Institute carries out should meaningfully integrate into fundamental contributions beyond the sum of the individual projects.
  • Each Institute will be comprised of multiple organizations working together to create significant new research capabilities. NSF and partner organizations seek to grow the network of National AI Research Institutes in lead organizations distributed throughout the country to grow new centers of AI leadership and leveraging existing centers of excellence as appropriate. Institutes are strongly encouraged to include organizations that can directly contribute to NSF’s commitment to broadening participation by engaging a diverse, globally engaged research community, integrating research with education and building capacity, and expanding efforts to include the participation of the full spectrum of diverse talent in STEM and diverse institutions across all geographical regions. Participants should be meaningfully integrated into a diverse Institute that is more than just the sum of the parts. Each Institute will have a lead PI with demonstrated vision, experience, and capacity to manage a complex, multi-faceted, and innovative enterprise that integrates research, education, broadening participation, and knowledge transfer. Each Institute will also be staffed with a Managing Director or Project Manager (distinct from the lead PI) and a suitable Management Team to oversee the operations of the Institute. An External Advisory Board is required for all AI Research Institutes. (Potential Advisory Board members should not be approached or identified until the Institute is funded.)
  • AI Research Institutes are nexus points for collaborative efforts. The “nexus point” function in this program is not a mere state of being, but rather an active set of priorities, programs, mechanisms, etc., whereby an AI Research Institute pursues the continuing growth of collaborations with external partners to bring together people, ideas, problems, and technical approaches for maximum impact beyond the members and the boundaries of the Institute itself. As nexus points, Institutes have the potential to continue to connect with new partners with the best teams and approaches from institutions of higher education, federal agencies, industry, nonprofits/foundations, centers/institutes, and national networks. As nexus points, Institutes promote organizational collaborations and linkages within and between campuses, schools, and the world beyond, and further the Institute’s mission to broaden participation in research, education, and knowledge transfer activities through a network of partners and affiliates.

II.B. Institute Themes in GROUP 1 Awards anticipated in FY 2024:

Proposals are being solicited in the following high-priority areas for awards anticipated in FY 2024. Due dates listed for Group 1 apply for submissions to the themes in this group.

Theme 1: AI for Astronomical Sciences

With current and future astronomical experiments poised to flood the field with petabytes of high-quality imaging and spectroscopic data over a wide range of wavelengths of light and with a high temporal cadence, AI technology will be essential for mining and analyzing these data. The primary goal of an AI Institute in astronomy is to bring together astronomy and AI experts to tackle important challenges in astronomy, as well as the advances in AI that are needed to overcome these challenges. An AI institute will serve as a hub and resource for the broader astronomical community by making tools publicly available and by promoting the education and training of the astronomical community in AI methods.

Proposals can address any relevant combination of AI use cases. Some examples are provided below. This list is meant to stimulate thought about the many potential application areas and is not prescriptive.

  • Clean raw astronomical imaging, spectroscopic, or time series data by removing sources of statistical and systematic noise.
  • Derive accurate estimates of physical parameters of objects or extract statistical measurements directly from raw observational data.
  • Classify objects on the fly for rapid follow-up observation.
  • Find rare events using anomaly detection.
  • Estimate physical model constraints directly from raw observational data.
  • Predict the behavior of complex theoretical simulations to reduce their computational cost.
  • Develop fast and accurate emulators that can be used in statistical modeling of data.
  • Create an “AI astronomer” who can assist with exploring multidimensional data sets or who knows the astronomical literature.

Many of these applications may require foundational advances in AI to succeed. For example, advances may be required in dealing with especially large and complex data sets, in adding knowledge of physical laws into AI models, or in developing interpretable AI methods with well understood error properties. Proposals should clearly justify both the selection of the targeted astronomical use cases and the breakthroughs needed in foundational AI research. Proposals are also encouraged to discuss the potential for those AI advances to benefit AI research more broadly or to impact application fields beyond astronomy.

Proposals are expected to convey a vision and approach that is appropriate for the scale of these Institutes and that produces transformative outcomes. Proposals should also describe how the Institutes will connect with the broader community to disseminate knowledge. The proposed structure, activities, and management of the Institutes to achieve these goals should be clearly described.

This theme is partially funded by the Simons Foundation. Each institute funded under this theme will receive two separate awards of up to $10M, one in the form of a cooperative agreement at NSF as described in this solicitation, and one award from SF in accordance with SF award procedures and consistent with applicable law. See Proposal Submission Guidelines for detailed procedures on how to structure project plans and budget submissions.

II.C. Institute Themes in GROUP 2 Awards anticipated in FY 2025:

Proposals are being solicited in the following high-priority areas for awards anticipated in FY 2025. Due dates listed for Group 2 apply for submissions to the themes in this group.

Theme 2: AI for Discovery in Materials Research

AI has the potential to revolutionize materials discovery by integrating first principles from materials science, physics, and chemistry with heterogenous multi-dimensional experimental and synthetic data streams to scale and accelerate development. AI can expand the types and properties of materials considered through augmentation of human intuition and by tailoring discoveries to address societal challenges, such as sustainability and those in emerging industries. A successful Materials AI Institute will transform the materials discovery landscape, enable new AI-based capabilities, and be responsive to societal challenges and industrial needs. Advances in AI have the potential to transform materials research in several ways. Some potential lines of research are provided below. This list is meant to stimulate thought about use-inspired research in the intersection of AI and materials, and is not prescriptive.

  • Multi-modal data integration and dataset development: Data streams that describe material properties and behaviors based on different types of variables are ubiquitous in materials science and span different length/time scales and represent a vast set of modalities, such as simulation, synthesis experiments, and characterization experiments. Research in AI-enabled frameworks for materials research have the potential to catalyze the generation of insights by integration of heterogeneous multi-modal data streams across different length/time scales. In addition, tools and mechanisms are needed to accelerate the development of new data sets with appropriate diversity, speed, and volume to empower ground-breaking AI methods for targeted materials science problems.
  • Foundational AI advances driven by materials research: Extending and tailoring AI methodologies to materials science and its unique data streams creates an opportunity to develop fundamentally new algorithmic and methodical frameworks in AI for materials discovery. From a bottom up (i.e., data-driven) direction, foundational AI advances in this field should fully capture and incorporate the unique characteristics and interactions evident in materials science. From a top down (i.e., knowledge-guided) perspective, the principles of materials science hold the potential to ground data-intensive operations in the rich mathematical complexity and multi-scale nature of the different physical and chemical relationships inherent to materials. The integration of both data-driven and knowledge-guided AI holds even greater potential to lead to significant advances in materials.
  • First synthesis to synthesis at scale: Materials synthesis at scale is a major challenge in materials discovery. The precision and level of understanding required spans various complex phenomenological challenges. Research in the intersection of materials science and AI has the potential to sustainably synthesize materials at scale while mitigating the complex phenomenological challenges related to materials properties, materials processing for reliable synthesis, efficient characterization for measurement of relevant properties, and statistics-based understanding of various stochastic elements present in large-scale systems. Use-inspired AI research for materials science has the potential to revolutionize materials discovery and lead to new technologies that can address complex societal challenges.
  • Human-augmented materials design: While AI holds great potential to automate discovery, it remains critical that this discovery be guided by and responsive to materials scientists who will collaborate with AI systems. The interfaces that mediate AI-driven materials research should be guided by principles for effective human-AI interaction and collaboration. Principled mechanisms of interaction between human experts and AI-augmented technology can change how materials designers think about design challenges and catalyze human creativity in new and unexpected ways—for example, shortening the requirements-design-synthesis-experiment cycle. Effective guidance from domain experts will also help ensure that the design of novel materials is conducted ethically and safely.
  • Interpretable materials AI: As AI accelerates new advances and insights in materials science, human understanding of materials will be advanced even further to the extent that the operations of the system are interpretable by materials scientists. A system with transparent and explicable operations will have a higher potential to contribute to the discovery of new fundamental principles in materials science. For example, might successful AI materials models predict the essential ingredients of microscopic Hamiltonians for quantum materials? Can they provide clues to develop new concepts that expand theory and computation to enable humans to reach the same or better solutions? The more interpretable the materials AI system, the greater the opportunity for materials scientists to explore new frontiers of research in this area.

Proposals to this theme can address these or other relevant research areas in any combination. Proposals that promise to significantly advance both foundational AI and domains supported by the Division of Materials Research will be most responsive to this theme.

Intel Corporation is providing partial support for this institute theme.

Theme 3: Strengthening AI

In recent years, AI systems built with multilayer architectures with many parameters trained on massive datasets have become increasingly capable of producing useful and impressive outputs. These developments have found their way into large scale deployment, while their developers continue to strive for higher levels of generality, performance, and trustworthiness. Deep neural networks are increasingly effective in all manner of applications from game playing to consumer recommendations to autonomous driving. Generative models have advanced significantly in their ability to produce constructions in natural language, images and video, leading to applications that automatically edit content or even produce novel images and texts. Unfortunately, these systems are not always reliable and may not exhibit justification for their behavior that is understandable to the people who interact with them. In spite of their limitations, these capabilities are becoming ubiquitous in fielded systems of all kinds. This trend presents the opportunity and necessity to research ways in which AI technologies of all sorts can be improved and integrated toward systems that are reliable and aligned with human intentions and ethical considerations.

A lens through which one might view the developments of AI systems is in terms of a continuum from narrow to general (or with similar meaning/intent, weak to strong). “Narrow AI” excels at performing specific tasks for which it has been programmed (or trained). Over the past few decades, these systems have far exceeded expectations in an increasing number of feats previously thought to be dominated by human intelligence. Still, these systems can be brittle in the face of surprising situations, susceptible to manipulation or anti-machine strategies, and produce outputs that do not align with human expectations of truth or human values. In contrast, “strong AI” is the aspirational goal of creating intelligent systems that learn and think as adeptly as humans do. Strong AI is expected to be capable of performing effectively in a diverse range of problems subject to potentially contradictory priorities, gain new conceptual understanding from limited exposure to new domains, and adapt appropriately to the expectations of human users. While such systems in principle would be more robust to situations that challenge narrow AI, no examples of strong AI have been demonstrated to date. Research in AI can no longer distinguish approaches simplistically as either narrow or strong. But AI systems of the future will need to be strengthened if they are to be as robust as we would like and if we are to keep such technology well-aligned with society’s intended uses.

Theme Goals:

This theme promotes the development of next generation AI systems that have been strengthened to provide greater usefulness, consistency, and robustness by exhibiting both the high performance of narrow AI and the general adaptability of strong AI. Proposals must address the following goals, taking into account the full context of the motivation described above, while remaining relevant to the contemporaneous, rapid progress in the fielding of large, capable AI models. Institutes funded under this theme must lead advances in theory, methods, or integrative approaches that strengthen AI in all three of the goals listed below:

1) Grounding. Systems must understand the concepts they reason over and operate with. We refer to this capacity as grounding. Grounding allows AI system to demonstrate connection between its outputs and the abstract concepts that they operate with. It will also enable systems to understand their risks and limitations. Such an improved conceptual understanding should also lead to robust AI that adapt gracefully and quickly to new domains, is robust to surprise, and resists malicious manipulation.

2) Instructiblity. Taking advantage of this firmer understanding, strengthened AI must be “instructible”. This means that systems can be proven experimentally to change their behavior appropriately in response to explicit feedback provided by even non-expert users. Related is how such instructible systems might invert the mechanisms behind this principle to implement more effective and trustworthy assistance to humans (e.g., in instruction, tutoring, and training) or in explaining their understanding or recommendations.

3) Alignment. Strengthened AI systems must be judged by how well their operations align with expectations of objective truths in a domain and correspond to societal expectations and human intentions in their operations. Proposals must include rigorous plans to evaluate this capability.

Any AI approaches that contribute to these three goals are in scope. This might include but is not limited to neuro-symbolic approaches, hybrid integrated architectures, or multi-representational learning methodologies. Proposals that rely mainly on continuing growth of data-driven models and their access to more data are not responsive to the three goals above unless accompanied by a compelling basis of confidence that true breakthroughs in those areas can be projected and evaluated. Technical approaches that integrate and process data from multiple sources and in diverse modalities as appropriate to the domain(s) of application are likely to serve the goals of this theme well. Institute concepts whose technical plans do not promise to advance all three of the above goals are unlikely to be competitive in this theme.

Use-Inspired research focus:

Any use-inspired research context may be the basis for an institute proposal to this theme. Institute research plans that strengthen AI in such a way that the techniques are generally applicable to diverse application domains are likely to serve the goals of this theme well. Proposers are encouraged to consider domains of broad significance to collective wellbeing. Examples of such domains include but are not limited to:

  • Protecting the environment to ensure human safety and to safeguard natural resources and wildlife.
  • Health and wellbeing, including various non-clinical aspects of physical and/or mental health.
  • Civic and public good, for example optimization of infrastructure, responsible resource allocation, delivery of public services.
  • Improving human flourishing, for example, reducing hunger or coordinating humanitarian assistance.
  • AI advances that enable new discoveries in science, mathematics, or engineering.
  • Enhancing the economic security of the U.S. through modernization of e.g., manufacturing, infrastructure, or communications.

Multiple awards are anticipated in this theme. Capital One is providing contributions for the partial funding of an award in this theme. Agency partners listed on this solicitation (OUSD (R&E) and NIST) may also elect to provide contributions to NSF for the funding of Institutes under this theme. Submitters to this theme may submit a supplementary document to indicate relevance of the proposed Institute to one or more partners. Submitters may also stipulate that the proposed Institute should not be considered for funding from specific partner(s) by uploading a single copy document. See Proposal Preparation Instructions.

Visit our Institutionally Limited Submission webpage for more updates and other announcements.