National Science Foundation (NSF)

NSF-23-521: Strengthening the Cyberinfrastructure Professionals Ecosystem (SCIPE)

Slots: 1

Deadlines

Internal Deadline: Friday, October 20th, 2023, 5pm PT

LOI: N/A

External Deadline: January 18, 2024;

Recurring Deadlines: Third Thursday in January, Annually Thereafter

Award Information

Award Type: Standard Grant or Continuing Grant or Cooperative Agreement

Estimated Number of Awards: 4

Anticipated Award Amount: $15,000,000

Who May Serve as PI: To ensure relevance to community needs and to facilitate adoption, those proposals of interest to one or more domain divisions must include at least one PI/co-PI with expertise relevant to the targeted research discipline. All proposals shall include at least one PI/co-PI with expertise relevant to OAC.

Link to Award: https://www.nsf.gov/pubs/2023/nsf23521/nsf23521.htm

Process for Limited Submissions

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 overarching goal of this solicitation is to democratize access to NSF’s advanced cyberinfrastructure (CI) ecosystem and ensure fair and equitable access to resources, services, and expertise by strengthening how Cyberinfrastructure Professionals (CIP) function in this ecosystem. It aims to achieve this by (1) deepening the integration of CIPs into the research enterprise, and (2) fostering innovative and scalable education, training, and development of instructional materials, to address emerging needs and unresolved bottlenecks in CIP workforce development. Specifically, this solicitation seeks to nurture, grow and recognize the national CIP ^[1] workforce that is essential for creating, utilizing and supporting advanced CI to enable and potentially transform fundamental science and engineering (S&E) research and education and contribute to the Nation’s overall economic competitiveness and security. Together, the principal investigators (PIs), technology platforms, tools, and expert CIP workforce supported by this solicitation operate as an interdependent ecosystem wherein S&E research and education thrive. This solicitation will support NSF’s advanced CI ecosystem with a scalable, agile, diverse, and sustainable network of CIPs that can ensure broad adoption of advanced CI resources and expert services including platforms, tools, methods, software, data, and networks for research communities, to catalyze major research advances, and to enhance researchers’ abilities to lead the development of new CI.

The SCIPE program is led by the Office of Advanced Cyberinfrastructure (OAC) in the Directorate for Computer and Information Science and Engineering (CISE) and has participation from other NSF directorates/divisions, as described in Section II. Program Description, Programmatic Areas of Interest. Not all directorates/divisions are participating at the same level, and some have specific research and education priorities. The appropriate contact for the SCIPE program in any directorate/division is the Cognizant Program Officer (PO) for the respective directorate/division/office/program listed below.

All projects are expected to clearly articulate how they address essential community needs, will provide resources that will be widely available to and usable by the research community, and will broaden participation from underrepresented groups. Prospective PIs are strongly encouraged to contact the Cognizant Program Officers in CISE/OAC and in the participating directorate/division relevant to the proposal to ascertain whether the focus and budget of their proposed activities are appropriate for this solicitation. Such consultations should be completed at least one month before the submission deadline. PIs should include the names of the Cognizant Program Officers consulted in a Single Copy Document as described in Section V.A. Proposal Preparation Instructions. The intent of the SCIPE program is to encourage collaboration between CI and S&E domain disciplines. (For this purpose, units of CISE other than OAC are considered domain disciplines.) To ensure relevance to community needs and to facilitate adoption, those proposals of interest to one or more domain divisions must include at least one PI/co-PI with expertise relevant to the targeted research discipline. All proposals shall include at least one PI/co-PI with expertise pertinent to OAC.

Prospective PIs contemplating submissions that primarily target communities relevant to directorates/divisions that are not participating in this solicitation are directed to explore instead the education and workforce development programs of the respective directorates/divisions.

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

NSF-21-628: Centers for Innovation and Community Engagement in Solid Earth Geohazards

Slots: 2. No more than two proposals across both tracks may be submitted by any Lead institution.

Deadlines

Internal Deadline: Friday, October 6th, 2023

LOI: November 16, 2023

External Deadline: March 15, 2024

Recurring Deadlines: March 15, Every Other Year Thereafter (LOI: November 16, Every Other Year Thereafter)

Award Information

Award Type: Standard Grant, Continuing Grant, or Cooperative Agreement

Estimated Number of Awards: 3 to 5

Anticipated Award Amount: $7,000,000

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

Link to Award: https://www.nsf.gov/pubs/2021/nsf21628/nsf21628.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)

Purpose

The Earth’s tectonics underpin many of the planet’s processes at a broad range of spatial and temporal scales, including earthquakes, volcanic eruptions, landslides and allied hazards. These solid Earth geohazards are responsible for trillions of dollars in damage and the loss of millions of lives in the history of the U.S., and these risks are anticipated to increase in the future as population increases in hazardous areas. Major advances in our understanding of the fundamental forces that drive catastrophic events are needed to respond effectively to this risk and to engage a well-informed populace living in the midst of these hazards.

The Centers for Innovation and Community Engagement in Solid Earth Geohazards are intended to catalyze, coordinate, and produce transformative research in the Earth processes that lead to solid Earth geohazards. Centers provide community-scale leadership in two areas: convergence and innovation in systems-level science, and community engagement to develop a diverse and inclusive workforce, as well as a well-prepared and informed public.

Centers will be built around a compelling research challenge or theme of significant scale and complexity that would require a coordinated approach beyond what can be accomplished by a small team of researchers. The program will support basic research on Earth processes, as well as the development of new methods needed to advance the science. The program will help facilitate new collaborations within observational, experimental, theoretical and computational domains that will be essential to address the most exciting questions at the cutting edge of Earth science. This program does not support efforts to operationalize monitoring, forecasting, or prediction of hazards, though partnerships could provide support for these activities.

Significant, long-term progress in advancing science requires a diverse and inclusive workforce that is highly skilled. Therefore, centers will also meaningfully improve the national welfare through bold and creative activities that broaden participation of underrepresented groups, develop a culture of equity and inclusion, and advance the geoscience workforce. The program’s focus on societally relevant problems provides a platform to engage and train students and postdocs from communities that have been underrepresented in science, technology, engineering, and mathematics (STEM). Additionally, knowledge-sharing between groups carrying out fundamental research and those focused on mitigating and forecasting future hazards is critical for the health and safety of our country. Consequently, Centers will expand the impact of fundamental research to a wide range of stakeholders.

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

NSF-23-613: Research Security and Integrity Information Sharing Analysis Organization (RSI-ISAO)

Slots: 1. An entity may serve as the lead organization on no more than one proposal.

Deadlines

Internal Deadline: Contact RII.

LOI: September 8, 2023, 5pm PT

External Deadline: October 30, 2023

Award Information

Award Type: Cooperative Agreement

Estimated Number of Awards: 1

Anticipated Award Amount: $9,500,000. The first year of funding will be up to $9,500,000 and will be committed upon award. Subsequent funding for years two through five, may be at least $10,000,000 per year, pending availability of appropriated funds, and subject to a satisfactory annual review of accomplishments relative to specified goals.

Who May Serve as PI: The principal investigator (PI) and co-PI(s) must be a U.S. citizen or lawful permanent resident.

Link to Award:

Process for Limited Submissions

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)

Purpose

NSF, through the Office of the Chief of Research Security Strategy and Policy (OCRSSP), seeks to establish an independent Research Security and Integrity Information Sharing Analysis Organization (RSI-ISAO) to empower the U.S. research community (institutions of higher education (IHEs), non-profit research institutions, and small and medium-sized for-profit organizations) to address foreign government interference, support security-informed decision-making, and serve as a conduit that connects research community stakeholders with one another and with U.S. government (USG) agencies via NSF.

We invite proposals that articulate a vision and actionable plan for the RSI-ISAO that would build the capacity of the research community to make risk-informed decisions and create a trusted partnership between USG research-awarding agencies and the research communities they serve. We invite proposers to identify strategic objectives to accomplish this vision consistent with the requirements set out in Section 10338 of the CHIPS and Science Act of 2022 (Public Law 117-167), enacted on August 9, 2022 (CHIPS and Science Act).

The RSI-ISAO’s principal duties include:

Serving as a clearinghouse for information;
Developing a set of standard risk assessment frameworks and best practices;
Providing timely reports on research security risks;
Providing training and support;
Enabling standardized information gathering;
Supporting analysis of patterns of risk and identification; and
Taking other appropriate steps to enhance research security.

These duties can be categorized using the following three functional domains in research security: (1) tools & training, (2) community engagement & inquiries, and (3) data analysis & reporting.

Informed by stakeholder engagement and statutory requirements, NSF has determined that the RSI-ISAO will:

Provide uniform quality of service to all members of the research community;
Respond to specific requests for assistance from research organizations, and from individual researchers through their organization or affiliated entity;
Handle unclassified information only; including publicly available information and declassified intelligence from USG agencies; and
Perform analyses and publish reports, based on information provided by USG agencies, research organizations, and the private sector, for the benefit of research stakeholders.

The RSI-ISAO will not:

Issue formal opinions, recommendations, or decisions to the research community;
Assume liability for the use of its products and services or the consequences arising from this use;
Issue policy;
Hold or analyze classified information; or
Conduct investigations.

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

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

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)

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.

(CLOSED) NSF-23-608: Predictive Intelligence for Pandemic Prevention Phase II (PIPP Phase II Centers Program)

Slots: 1. An organization may submit no more than one Letter of Intent to this solicitation as lead organization. An organization may submit up to one full proposal that corresponds to a Letter of Intent submitted to this solicitation. A full proposal that does not correspond to a Letter of Intent submitted to the program will be returned without review.

Deadlines

Internal Deadline: ~~Research Deans to contact RII with any interested parties by Monday, July 31st, 2023.~~ Closed.

LOI: August 25, 2023 (required)

External Deadline: December 8, 2023

Award Information

Award Type: Cooperative Agreement

Estimated Number of Awards: 4 to 7

Anticipated Award Amount: $126,500,000

Who May Serve as PI:

The Lead PI must be a faculty member or equivalent at the lead organization. A letter of commitment from the Dean or equivalent at the lead organization must be submitted as part of the proposal given the broad focus of the centers.

NOTE: Submission or award of a Development Grant (PIPP Phase I) is not required to participate in the PIPP Phase II Centers Program competition

Link to Award: https://www.nsf.gov/pubs/2023/nsf23608/nsf23608.htm

Process for Limited Submissions

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)

Purpose

Building on visioning exercises and the PIPP Phase I Development Grant Program, the PIPP Phase II program will fund Centers comprised of scientists, engineers, practitioners, and/or educators united by a common focus on advancing the research frontiers of epidemic and pandemic preparedness and national and global health security. The program seeks to build a broad, nationwide set of centers to pursue transformational advances in a range of science and engineering fields – see Center Themes, below. PIPP Phase II Centers will have as their primary focus the advancement of multidisciplinary, multi-stakeholder pandemic research on larger-scale, longer-time-horizon challenges than are supported in typical research grants.

Against this broad and ambitious scope, it is expected that each Center will pursue their vision in ways that are best suited to its selected research focus, facilities, collaborations, and other unique circumstances. Proposers are encouraged to convey the unique qualities of the proposed Center, while addressing the following:

Centers are coherent multidisciplinary groups of scientists, engineers, practitioners, and/or educators appropriate for a large-scale, long-term research agenda for the advancement of pandemic research and innovation in application sectors of national importance. The multidisciplinary nature of these Centers will catalyze coordinated, convergent, and nimble responses to pandemics and pandemic research needs that are not hindered by silos and lack of shared data. Additionally, the individual projects that a Center may carry out should meaningfully integrate into fundamental contributions beyond the sum of the individual projects.
PIPP Phase II Centers innovate and advance foundational research that will have broad and lasting impact, contributing new knowledge or methods toward understanding the rise, spread and response to pandemics (see the program vision specified above). Centers aimed at advancing established lines of research should demonstrate the potential to radically advance these areas beyond the state of the art. For Phase II Center proposals that arise from a PIPP Phase I Development grant, proposals are expected to address how new foundational research and development priorities arose and advanced from previous funding. PIPP Phase II Center proposals that do not describe a clear plan to achieve ambitious advances in foundational research will not be responsive to this solicitation. It is critical that proposals clearly specify how the use-inspired context for Center research reveals the opportunities for foundational advances and how those foundational advances in turn contribute to the related sectors that define the use-inspired context. This dimension of a center-scale proposal will feature clear and compelling goals to enhance the transfer of knowledge through the meaningful exchange of scientific and technical information with external stakeholders such as industrial partners, public policy makers and users, or international organizations, as well as with the broader scientific and educational community. Through use-inspired contexts, Centers have the potential to create and share new community infrastructure, including data and software and/or analytical capacity, to further research, promote data and research reproducibility, and support education.
PIPP Phase II Centers actively build the next generation of talent for a diverse, well-trained workforce. Specifically, a Center 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 pandemic related research. This could include innovative pedagogy, curricula and instructional materials, advanced learning technologies, project-driven training, cross- disciplinary and collaborative research, industry partnerships, and new career pathways. Centers should offer broad, deep, and diverse experiences to build the next generation of a workforce ready to deal with pandemic threats, with a focus on broadening participation among the full range of groups currently under-represented in science and engineering. Centers should maximize their unique position to grow the next generation of talent that will provide new discoveries and leadership.
Each Center will be comprised of multiple organizations/entities working together to create significant new research capabilities. NSF seeks to grow new Centers of pandemic research leadership throughout the country and encourages leveraging existing Centers of excellence as appropriate. Centers are strongly encouraged to include organizations that 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 Center. Each Center 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 Center 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 Center.
PIPP Phase II Centers 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 a Center 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 Center itself. As nexus points, Centers 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, Centers promote organizational collaborations and linkages within and between campuses, schools, and the world beyond, and further the Center’s mission to broaden participation in research, education, and knowledge transfer activities through a network of partners and affiliates.

Center Themes

Building on previous visioning exercises and funding opportunities through the PIPP Phase I Developmental Grant effort, in this round, proposals are being solicited in the following high-priority areas, described below. The descriptions for each theme are only intended to provide a broad, principal focus, they are not exhaustive reviews of respective fields. Also, while each theme has a clear center of gravity in some disciplines, successful Center-scale proposals will integrate and innovate across all relevant areas of environmental, biological, chemical, physical, materials, social, behavioral, economic, mathematical, computer and information science and engineering science, and reflect this in balanced expertise on the team of collaborators and participants. Full proposal submissions MUST identify as a principal focus one of the themes described below. This principal theme must be reflected in the Title of the Project. However, the themes below are not mutually exclusive, and it is expected that connectivity exists between themes.

Theme 1: Pre-emergence – Predicting and detecting rare events in complex, dynamical Systems

Natural and social systems are dynamic, stochastic, nonlinear, multi-scale and complex, making both the theoretical prediction and modeling of its states challenging. Some examples in a predictive context include: epidemiological transitions from a pre-emergence stage to localized emergence (epidemic) and/or a pandemic; transition of an individual or a community from resistant to susceptible to disease; transition of a microbe from harmless to pathogenic.

Usually, these systems spend protracted periods of time in various metastable states and only very rarely, and at seemingly random times, transition to other states, often producing effects that cross spatial and organismal scales in the process. Predicting the dynamics of such systems across scales requires the development of frameworks of contextual predictive intelligence to understand how transition paths interact with a series of additional and often seemingly unrelated events and/or dynamic variables.

One challenge to innovation in data-driven and contextual predictive intelligence for pandemics is the need to develop Artificial Intelligence (AI)- and machine learning (ML)-based computational approaches that meaningfully analyze multiple biological data points (e.g., from bio-surveillance, host-pathogen interactions, at scale ecology) to timely (real-time, just-in-time) identify events that are biologically relevant and significant for pandemics. This will require robust mathematical and physical models, and new biologically-cognizant computational approaches to (1) detect and analyze data that are being collected continuously and across scales, and (2) identify the key events (which may appear unrelated at the onset) that precede pandemics. Another, related challenge is the integration of data-driven approaches with mechanistic models to facilitate our understanding of the biological underpinnings of the complex process of emergence. This will require integrating molecular to environmental scale data and models, with novel computational systems to identify biological, environmental, and/or sociological drivers of rare events and transition states that influence emergence.

Theme 2: Data, AI/Machine Learning and Design – Computing, sensing, manufacturing and technology innovation for pandemics

With the current and emerging challenges of increased spill-over risk of pathogens it is important that a paradigm shift occurs in how pathogens and diseases are identified across ecosystems and hosts in either real time, or times well ahead of outbreaks (see Theme 1 above). This shift involves improving the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems. On the data collection and information integration domain, most available technologies are designed for humans. Move towards new ideas to develop methods and devices grounded in advances of wireless technologies, computing software and hardware, algorithms, and machine learning that enable data monitoring, acquisition, and analysis for animal hosts and humans is warranted. We envision that information gained on changes in animal behavior and welfare, physiology, or range to intelligently and timely identify pre-pandemic conditions.

Another critical area of research to enhance prediction is in computational algorithms and frameworks for processing, analyzing, and modeling data to enable inference of difficult-to-measure information and integration of traditional computing and biological computing platforms. This highlights the effort needed to connect measurable with difficult/unmeasurable (observable versus unobservable) biological events/characteristics/processes for pre-emergence forecasting of pandemics. Progress in biological sensor technologies, in-situ sensors, and smart sensor networks to record difficult-to-measure or track in near real time cellular and molecular-scale effects as well as to monitor biological systems at all scales is critical. Monitoring systems at a higher level for effective and comprehensive disease emergence and spread prediction/analysis could benefit from coordinated solutions of in-situ detection, computation, and decision by networked sensors, actuators and processors, and physical processes, together with cybersystems (such ecosystems could be modeled as cyber-physical systems) of simulated events where the interactions between levels and systems can be simulated to enhance, understand, and generate empirically based policy. An important challenge and opportunity to advance in this field calls for a continuous and iterative feedback loop between simulated and real worlds, which must happen in either real time or at relevant time scales.

The development and strengthening of computational methods and process of data collection, analysis, storage, curation, and analytics, along with a “model commons” serving as a repository of leading-edge modeling and simulation about pathogens and pandemic disease is the basis for a successful predictive intelligence for pandemic prevention program. On the other hand, the increasing number of available models and the lack of best practices in coordination, integration and data sharing are major roadblocks in the development of the field. This solicitation calls for a clearly rationalized research plan that addresses these concerns at all steps in the design of computing and networked systems/infrastructure for predictive intelligence for pandemic prevention.

Theme 3: The Host as the Universe – Identifying host-pathogen tipping points that dictate control or spread of an infection

The host can be the bottleneck or the amplifier of a pathogen. The outcome of infection may change in the face of pathogen mutation and/or changes in the host environment. A concerted research effort spanning multiple research areas is required to understand the basis for host control in the spread of an infection under these changing conditions. Research submitted under this theme is expected to pursue critical aspects of molecular, cellular and physiological mechanisms that dictate the outcome of host-pathogen interactions, including disease outbreaks. This foundational knowledge could be exploited to inform insights to control or predict pathogen emergence, identify new therapeutics or vaccines, or predict and understand the emergence and maintenance of antimicrobial resistance.

A game-changer in pandemic prevention would be remote sensors that autonomously track and report on pathogens and their hosts in situ over time and space. Remote tools and technologies for detecting and monitoring changes in biological systems and processes that are also capable of analyzing and transmitting information from hosts could make possible the discovery of potential for outbreaks prior to society-scale dissemination. Although genome sequencing is now ubiquitous it remains difficult to use this sequence (or other -omics based methods) to accurately predict hosts for a new pathogen, or to detect transmission and amplification routes of a new pathogen. This challenge could be overcome by advances in engineering and in computational algorithms and frameworks for processing, analyzing and modeling data.

Molecular-level information exists about host responses to infection and about the bio-synthetic pathways required for a pathogen to survive in a specific host. This information can be leveraged to develop therapeutic and diagnostic tools. There is still a gap in understanding how these functions occur temporally and in concert within the entire host organism, and the hierarchy of mechanisms that result in control or spread of an infection within an individual and throughout homo- and heterogeneous hosts. An opportunity to advance in this field is developing mathematical and computational models that accurately predict infection outcomes from sub-cellular events.

Theme 4: Human Systems – The role of human behavior, activities and environment in disease emergence, transmission, response and mitigation.

The devastating human, economic, and social costs of the recent pandemic have highlighted the urgent need for coordinated research and actions to build resilient systems for pandemic prevention, preparedness, and response. Central to the development of resilient systems is a recognition of the role that humans and their environments play in disease and pandemic dynamics and an understanding of the complex behavior of humans at individual, group, organizational, societal, and global scales.

For the purpose of this solicitation, the term environment is broadly construed, it may include biological, physical and chemical components, and may consider a continuum of environments from those with very limited human populations (e.g., “natural” environments) to those in which human systems and processes fully dominate (e.g. cities, built environments). Research that meaningfully incorporates social and behavioral processes in epidemiological models relevant to human systems and environments is particularly critical.

Center projects submitted to this theme will therefore advance science and engineering research on human systems that are relevant to improved pandemic prevention and resilience. It is expected that research in this theme will be necessarily interdisciplinary and explore aspects of how human cognition, behavior and attitudes, and the sociocultural and socio-environmental drivers underlying these, contribute to disease emergence, transmission and mitigation. Research under this theme must address human behavior and/or social processes, and projects may, but need not, focus on human pathogens or human hosts. Understanding the human impact will also allow for the development and optimization of high impact methods for disease surveillance/prevention/suppression/containment/mitigation protocols. Examples of broad areas of focus that leverage and advance foundational research in science and engineering and support use-inspired goals include, but are not limited to: investigating how the environment and/or human attitudes, beliefs, decisions, and activity patterns contribute to disease transmission and affect pandemic resilience and responses; understanding the processes in water, soil, and/or air that trigger or amplify pathogen spread in human environments; understanding the role of organizations and policymakers in communicating disease risk, coordinating across stakeholders, shaping research efforts, and planning and implementing disease mitigation protocols; and utilizing and advancing sociocultural and economic models and theories about the ethical and logistical trade-offs between human health/privacy and economic/societal health in order to optimize disease prevention and mitigation strategies.

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

NSF-23-527: NSF Scholarships in Science, Technology, Engineering, and Mathematics (S-STEM)

Slots: Two.

An institution may submit up to two proposals (either as a single institution or as a subawardee or a member of an inter-institutional consortia project (lead or co-lead) for a given S-STEM deadline. Multiple proposals from an institution must not overlap with regard to S-STEM eligible disciplines. See Additional Eligibility Information below for more details (see IV. Eligibility Information).

Institutions with a current S-STEM award should wait at least until the end of the third year of execution of their current award before submitting a new S-STEM proposal focused on students pursuing degrees in the same discipline(s).

The above restrictions do not apply to collaborative planning grant proposals.

Deadlines

Internal Deadline: Friday, October 13, 2023, 5pm PT

External Deadline: February 20, 2024 for Tracks 2, 3, & Collaborative Planning Grants; March 28, 2024 for Track 1 proposals

Recurring Deadlines: For Tracks 2, 3, & Collaborative Planning Grants: Third Tuesday in February, Annually Thereafter;

March 28, 2024 for Track 1 proposals and Fourth Thursday in March, Annually Thereafter

Award Information

Award Type: Grant

Estimated Number of Awards: 50-90

Anticipated Award Amount: $80,000,000 to $120,000,000

Awards for Track 1 (Institutional Capacity Building) projects may not exceed $1,000,000 total for a maximum duration of 6 years.

Awards for Track 2 (Implementation: Single Institution) projects may not exceed $2,500,000 total for a maximum duration of 6 years.

Awards for Track 3 (Inter-institutional Consortia) projects may not exceed $5,000,000 total for a maximum duration of 6 years.

Collaborative Planning projects may not exceed $100,000 for a maximum duration of 1 year.

Who May Serve as PI:

For Track 1 (Institutional Capacity Building) and Track 2 (Implementation: Single Institution) projects, the Principal Investigator must be (a) a faculty member currently teaching in an S-STEM eligible discipline, or (b) an academic administrator who has taught in an S-STEM eligible discipline in the past two years. The Principal Investigator must be able to provide the leadership and time required to ensure the success of the project. Projects involving more than one department within an institution are eligible, but a single Principal Investigator must accept overall management and leadership responsibility. Faculty from all departments involved need to have roles in the project as either Co-Principal Investigators, senior personnel or scholar mentors. Other members of the S-STEM project senior leadership and management team may be listed as Co-Principal Investigators.

For Track 3 (Inter-institutional Consortia) projects, the Principal Investigator must be (a) a faculty member currently teaching in an S-STEM eligible discipline, (b) an academic administrator who has taught an S-STEM eligible discipline in the past two years, or (c) a non-teaching institutional, educational, or social science researcher investigating questions related to low-income student success. The Principal Investigator must be able to provide the leadership and time required to ensure the success of the project. Track 3 consortium proposals must have a Principal Investigator who accepts overall management and leadership responsibility across all consortia members. Faculty from all institutions and departments involved need to have roles in the project as either Co-Principal investigators, senior personnel or scholar mentors. Other members of the S-STEM project senior leadership and management team may be listed as Co-Principal Investigators or as Principal Investigators on collaborative research proposals.

Collaborative Planning grants are intended to help a collection of institutions plan for a future Inter-institutional Track 3 proposal. For Collaborative Planning grants, the Principal Investigator must be (a) a faculty member teaching in any S-STEM eligible discipline, or (b) a STEM administrator (department head or above) at one of the institutions within the envisioned inter-institutional consortia, or (c) a non-teaching institutional, educational, or social science researcher investigating questions related to low-income student success. The Principal Investigator on a Collaborative Planning grant must demonstrate the capacity to convene and lead a team of inter-institutional S-STEM eligible faculty, social science or educational researchers, and administrators focused on low-income student success to write the desired proposal in a 1-year timeframe

Link to Award: https://www.nsf.gov/pubs/2023/nsf23527/nsf23527.htm

Process for Limited Submissions

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)

Purpose

The main goal of the S-STEM program is to enable low-income students with academic ability, talent or potential to pursue successful careers in promising STEM fields. Ultimately, the S-STEM program seeks to increase the number of low-income students who graduate with a S-STEM eligible degree and contribute to the American innovation economy with their STEM knowledge. Recognizing that financial aid alone cannot increase retention and graduation in STEM, the program provides awards to institutions of higher education (IHEs) not only to fund scholarships, but also to adapt, implement, and study evidence-based curricular and co-curricular[1] activities that have been shown to be effective supporting recruitment, retention, transfer (if appropriate), student success, academic/career pathways, and graduation in STEM.

Social mobility for low-income students with academic potential is even more crucial than for students that enjoy other economic support structures. Hence, social mobility cannot be guaranteed unless the scholarship funds the pursuit of degrees in areas where rewarding jobs are available after graduation with an undergraduate or graduate degree.

The S-STEM program encourages collaborations, including but not limited to partnerships among different types of institutions; collaborations of S-STEM eligible faculty, researchers, and academic administrators focused on investigating the factors that affect low-income student success (e.g., institutional, educational, behavioral and social science researchers); and partnerships among institutions of higher education and business, industry, local community organizations, national labs, or other federal or state government organizations, as appropriate.

Scholars must be domestic low-income students, with academic ability, talent or potential and with demonstrated unmet financial need who are enrolled in an associate, baccalaureate, or graduate degree program in an S-STEM eligible discipline. Proposers must provide an analysis that articulates the characteristics and academic needs of the population of students they are trying to serve. NSF is particularly interested in supporting the attainment of degrees in fields identified as critical needs for the Nation. Many of these fields have high demand for training professionals that can operate at the convergence of disciplines and include but are not limited to quantum computing and quantum science, robotics, artificial intelligence and machine learning, computer science, data science and computational science applied to other frontier STEM areas and other STEM or technology fields in urgent need of domestic professionals. It is up to the proposer to make a compelling case that a field is a critical need field in the United States.

S-STEM Eligible Degree Programs

Associate of Arts, Associate of Science, Associate of Engineering, and Associate of Applied Science
Bachelor of Arts, Bachelor of Science, Bachelor of Engineering and Bachelor of Applied Science
Master of Arts, Master of Science and Master of Engineering
Doctoral

S-STEM Eligible Disciplines

Disciplinary fields in which research is funded by NSF, with the following exceptions:
1. Clinical degree programs, including medical degrees, nursing, veterinary medicine, physical therapy, and others not funded by NSF, are ineligible degrees.
2. Business school programs that lead to Bachelor of Arts or Science in Business Administration degrees (BABA/BSBA/BBA) are not eligible for S-STEM funding.
3. Masters and Doctoral degrees in Business Administration are also excluded.
Technology fields associated with the S-STEM-eligible disciplines (e.g., biotechnology, chemical technology, engineering technology, information technology).

Proposers are strongly encouraged to contact Program Officers before submitting a proposal if they have questions concerning degree or disciplinary eligibility.

The S-STEM program particularly encourages proposals from 2-year institutions, Minority Serving Institutions (MSIs), and urban, suburban and rural public institutions.

[1] an activity at a school or college pursued in addition to the normal course of study.

Description of Program Tracks: The following sections describe each track differences:

Track 1 (Institutional Capacity Building)

Track 1 projects seek to increase the participation of institutions that have never had an award from the S-STEM program or the STEM Talent Expansion (STEP) program. This requirement applies to the institution as a whole. One S-STEM or STEP award to any department or school within the institution makes the entire institution ineligible for a Track 1 award.

Track 1 projects must be led by a PI who is (a) a faculty member currently teaching in one of the S-STEM eligible disciplines being pursued by the targeted scholars, or (b) an academic administrator who has taught in one of the eligible disciplines within the two years prior to submission and can dedicate the time necessary to assure project success. The PI must be a member of the proposed project’s leadership and management team. The leadership and management team should also include a STEM administrator (department head or above). Faculty members from all departments or academic units involved should have a role in the project either as Co-PIs, senior personnel, or scholar mentors. The project team could include, if appropriate, a non-teaching institutional, educational, or social science researcher to support evidence-based responses to items raised by the external evaluator through formative evaluation. This additional researcher cannot take the place of the external evaluator.

Track 1 proposals may also include a focus on student transfer or progression to graduate school. In this case, if needed, two or more institutions could partner.

Track 1 proposals may request up to $750,000 total for up to 6 years.

Track 2 (Implementation: Single Institution)

Track 2 proposals have the same S-STEM goals as Track 1 proposals. They generally involve and benefit only one institution, but they will serve more scholars than Track 1 proposals. Any IHE (as described under the eligibility section) can submit a Track 2 proposal, whether or not the institution has received prior S-STEM or STEP awards.

Track 2 proposals may, in some cases, also include a focus on student transfer or progression to graduate school. In this case, if needed, two or more institutions could partner.

Track 2 projects must be led by a PI who is (a) a faculty member currently teaching in one of the S-STEM eligible disciplines being pursued by the targeted scholars, or (b) an academic administrator who has taught in one of the eligible disciplines in the last two years from submission and can dedicate the time necessary to assure project success. The PI must be a member of the proposed project’s leadership and management team. The leadership and management team should also include a STEM administrator (department head or above). Faculty members from all departments or academic units involved should have a role in the project either as Co-PIs, senior personnel, or scholar mentors. The project team could include, if appropriate, a non-teaching institutional, educational, or social science researcher to support evidence-based responses to items raised by the external evaluator through formative evaluation. This additional researcher cannot take the place of the external evaluator.

Proposals for Track 2 may request up to $1,500,000 total for up to 6 years.

Track 3 (Inter-institutional Consortia)

Track 3 projects support multi-institutional collaborations that focus on a common interest or challenge. For example, a collaboration among community colleges and four-year institutions may focus on issues associated with successful transfer of low-income students from 2-year institutions to 4-year programs. In another example, a multi-institutional collaboration may focus on investigating factors, such as self-efficacy or identity, which contribute to the success or degree attainment of domestic, low-income students in different types of institutions.

Proposals with a strong focus on the transfer or advancement of students from one educational level to another should collaborate with appropriate institutional partners. For example, proposals focused on the transfer of students from 2-year institutions to 4-year institutions should include faculty and administrators from 2-year institutions and 4-year institutions in the leadership team; likewise, proposals focusing on the advancement of undergraduate students at predominately undergraduate institutions to graduate programs should include institutions, administrators and Co-PIs representing both the undergraduate programs and the receiving graduate programs.

Track 3 projects have the same overall goals as Track 1 and 2 projects but seek to accomplish these goals at a very large scale by leveraging multi-institutional efforts and infrastructure. In addition to the expectations stated in section II.B.2 for all tracks, Track 3 projects are expected to:

Establish an authentic, strong and mutually beneficial collaboration across all institutions involved in the consortia, providing comparable benefits to all institutions in terms of number of scholarships as well as in the infrastructure established to serve low-income students;
Establish strong technical assistance and processes that support and manage project activities across institutions involved in the collaborative effort.
Engage in high quality research to advance understanding of how to adapt, implement and scale up effective evidence-based programs and practices designed to foster positive outcomes for low-income students in STEM.

NSF does not favor a particular research design over others. How the chosen research methods and approaches are aligned with and appropriate for the research goals should be fully explained in the proposal. The ultimate goal of S-STEM is to support low-income students. Projects are strongly discouraged from allowing a desired sample size to play a role in the determination of the size of awarded scholarships.

Track 3 projects are managed by leadership and management teams composed of faculty members who are currently teaching in an S-STEM eligible discipline(s), STEM administrators, and non-teaching institutional, educational, or social science researchers. The PI of Track 3 proposals must be either (a) a faculty member currently teaching in one of the S-STEM eligible disciplines, (b) a STEM administrator (department head or above), or (c) a non-teaching researcher whose expertise is in institutional, educational, or social science research in higher education. Faculty from all the institutions and disciplines involved need to be included in the leadership team and/or senior personnel. The lead PI needs to demonstrate the capacity, experience and resources needed to manage a complex, large-scale project and the necessary time to dedicate to assure project success.

Track 3 proposals may request up to $5 million total for up to 6 years.

Track 3 projects will be reviewed by NSF during their third year to determine whether satisfactory progress has been made, with continued funding contingent on the result of the third-year review. See section VII.C on reporting requirements.

Collaborative Planning Grants to Develop an Inter-institutional Consortium

Collaborative Planning projects provide support for groups of two or more IHEs and other potential partner organizations to establish fruitful collaborations, increase understanding of complex issues faced by low-income students at each institution, establish inter-institutional agreements when necessary and develop mechanisms for cooperation in anticipation of a future Track 3 proposal that will benefit all institutions and their scholars as equal partners.

This category of projects aims to provide proposers from two or more institutions the funds and time to establish the relationships and agreements necessary for submitting an Inter-institutional Consortia S-STEM proposal. It is expected that proposers will be ready to write and submit this Inter-institutional Consortia proposal within 1-2 years of receiving a Collaborative Planning grant award. Any subsequent proposals to S-STEM based on this work must describe the results of the planning effort.

Inter-institutional Consortia projects represent diverse collaborations, including partnerships between 2-year colleges and 4-year colleges and universities, between 4-year colleges and graduate programs, or between comparable institutions looking to implement and study parallel interventions. As such, Collaborative Planning grants can address these, or other, types of partnerships that might result in a stronger Track 3 proposal. Ideally, planning grants should reflect authentic collaborations between institutions, prepare collaborative partners to award scholarships at all collaborating institutions and provide programming according to each institution’s needs assessment and realities.

A Collaborative Planning grant should allow institutions to gather data, design shared mechanisms for data collection and student support, and establish the necessary memorandum of understanding (MOUs) or articulation agreements to facilitate students’ transition between institutions and ultimate success. Different methodological approaches may be employed to uncover the needs across institutions. PIs should propose approaches they feel are appropriate to their specific context. Surveys, focus groups, interviews, etc., can also be included in the planning grant as mechanisms to understand the needs of students. Furthermore, Collaborative Planning proposals must include the following elements in the project description:

what is already known about all potential partner institutions;
the planning grant goals;
name of the individuals and offices that will be approached at each institution and description of the potential contributions of collaborators representing multiple perspectives;
the steps to build effective collaborations to achieve the project goals (needs assessment, articulation agreements; meetings, etc.);
the steps and actions to further refine and develop the future S-STEM Track 3 proposal, including how programmatic details will be decided (the interventions, the definition of the scholarship eligibility requirements based on institutional data; establishment of scholarship amounts, and methods), leveraging the expertise of the collaborators;
narrative of how the development of the collaboration will lead to a stronger future Track 3 proposal, and;
a mechanism to assess the collaborative planning effort’s progress towards its stated goals.

If appropriate, Collaborative Planning Grant proposals may request funds to pilot evidence-based supports at one or more institutions in order to collect preliminary data and strengthen those activities. Participating institutions can also test new policies and administrative procedures that, per a needs assessment or other institutional data, have potential to remove barriers or otherwise improve outcomes for potential S-STEM scholars.

Please note that, while collaborative planning projects may wish to share any findings or implementation mechanisms, a formal dissemination plan is not required.

Collaborative planning grants are managed by a PI who is either (a) a faculty member teaching in any S-STEM eligible discipline, (b) STEM administrator (department head of above) at one of the institutions within the envisioned inter-institutional consortia, or (c) a non-teaching researcher whose expertise is in institutional, educational, or social science research in higher education. The PI must provide the required leadership and the capacity to convene and lead a team of inter-institutional STEM faculty and social science or education researchers to write the desired proposal in a 1-2-year timeframe. A successful Track 3 proposal will likely require a range of expertise including STEM faculty and administrators at all institutions, financial aid officers, and education, learning science or social science researchers interested in low-income student success or other pertinent topics. It is ideal that management of the planning grant incorporate the appropriate senior personnel across institutions as needed. Planning grants can also speak to potential gaps in expertise that might hinder a forthcoming Track 3 proposal and work to identify and build relationships with qualified individuals or organizations that would enhance the impact of future collaborative efforts.

Please note that the Collaborative Planning Grant proposals described in this solicitation are a solicitation-specific project category and are separate and distinct from the type of proposal described in Chapter II.E.1 of the PAPPG. When preparing a Collaborative Planning Grant proposal in response to this solicitation, the “Research” type of proposal should be selected in the proposal preparation module in FastLane or Grants.gov.

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