The DIFFERENTIATE program seeks to enhance the pace of energy innovation by incorporating machine learning into energy technology development processes. By doing so, this program aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. In order to organize the proposed efforts, a simplified engineering design process framework has been adopted and utilized to identify several general mathematical optimization problems that are common to many (perhaps most) engineering design processes and then to conceptualize several machine learning tools that could help engineers to execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation. In this framework: 1. A problem/challenge is posed (e.g. cost-effectively generate electricity from natural gas at an efficiency in excess of 70%), 2. A potential solution is hypothesized and refined with Reduced Order Models (e.g. fuel cell/engine hybrid systems), 3. The low fidelity concept is further refined and evaluated with high-fidelity partial differential equation-based solvers and/or experiments (e.g. computational fluid dynamics simulations, finite element analyses, full-scale system demonstrations), and 4. The hypothesized solution is updated with knowledge gained during the high-fidelity evaluation process, and iteration continues until either the problem is either solved or deemed insoluble. The objective of the DIFFERENTIATE program is to enhance the pace of energy innovation by accelerating the incorporation of machine learning into the energy technology design process. Specifically, ARPA-E is seeking to develop machine-learning-enhanced— 1. Hypothesis generation (i.e. Conceptual Design) tools, 2. High-fidelity hypothesis evaluation (i.e. Detailed Design) tools, and 3. Inverse design tools. In order to facilitate the achievement of the above-mentioned objective, ARPA-E is issuing this FOA to encourage teams consisting of mathematicians, operations research analysts, computer scientists, energy engineers, and others with applicable skills and experience to jointly work on developing the tools required to enhance the creativity and efficiency (i.e. productivity) of the energy technology design process.
Concept paper: 5/20/2019
The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in receiving applications from large multi-disciplinary teams (requesting support of more than $2 million per year) with the potential to dramatically accelerate research in quantum computing (QC). This FOA solicits applications for Accelerated Research in Quantum Computing (ARQC) Teams that will adopt a holistic and disciplined approach to address basic research gaps in the abstractions, methods and tools that connect QC applications to hardware.
The Secure and Trustworthy Cyberspace (SaTC) program welcomes proposals that address cybersecurity and privacy, and draw on expertise in one or more of these areas: computing, communication and information sciences; engineering; economics; education; mathematics; statistics; and social and behavioral sciences. Proposals that advance the field of cybersecurity and privacy within a single discipline or interdisciplinary efforts that span multiple disciplines are both encouraged. Through this solicitation—under the SaTC umbrella—NSF specifically seeks ambitious and potentially transformative center-scale projects in the area of security and privacy that (1) catalyze far-reaching research explorations motivated by deep scientific questions or hard problems and/or by compelling applications and novel technologies that promise significant scientific and/or societal benefits, and (2) stimulate significant research and education outcomes that, through effective knowledge transfer mechanisms, promise scientific, economic and/or other societal benefits.
NSF has long supported transformative research in artificial intelligence (AI) and machine learning (ML). The resulting innovations offer new levels of economic opportunity and growth, safety and security, and health and wellness. At the same time, broad acceptance of large-scale deployments of AI systems relies critically on their trustworthiness which, in turn, depends upon the collective ability to ensure, assess, and ultimately demonstrate the fairness, transparency, explainability, and accountability of such systems. Importantly, the beneficial effects of AI systems should be broadly available across all segments of society. NSF and Amazon are partnering to jointly support computational research focused on fairness in AI, with the goal of contributing to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society. Specific topics of interest include, but are not limited to transparency, explainability, accountability, potential adverse biases and effects, mitigation strategies, validation of fairness, and considerations of inclusivity. Funded projects will enable broadened acceptance of AI systems, helping the U.S. further capitalize on the potential of AI technologies. Although Amazon provides partial funding for this program, it will not play a role in the selection of proposals for award. Advancing AI is a highly interdisciplinary endeavor drawing on fields such as computer science, information science, engineering, statistics, mathematics, cognitive science, and psychology. As such, NSF and Amazon expect these varied perspectives to be critical for the study of fairness in AI. NSF's ability to bring together multiple scientific disciplines uniquely positions the agency in this collaboration, while building AI that is fair and unbiased is an important aspect of Amazon’s AI initiatives. This program supports the conduct of fundamental computer science research into theories, techniques, and methodologies that go well beyond today's capabilities and are motivated by challenges and requirements in real systems.
Anthropological research may be conducted under unusual circumstances, often in distant locations. As a result the ability to conduct potentially important research may hinge on factors that are impossible to assess from a distance and some projects with potentially great payoffs may face difficulties in securing funding. This program gives small awards that provide investigators with the opportunity to assess the feasibility of an anthropological research project. It is required that the proposed activity be clearly high risk in nature. The information gathered may then be used as the basis for preparing a more fully developed research program. Investigators must contact the cognizant NSF Program Director before submitting an HRRBAA proposal.
Proposal: any time
NSF seeks to strengthen the future U.S. Engineering workforce by enabling the participation of all citizens through the support of research in the science of Broadening Participation in Engineering (BPE). The BPE program is a dedicated to supporting the development of a diverse and well-prepared engineering workforce. BPE focuses on enhancing the diversity and inclusion of all underrepresented populations in engineering, including gender identity and expression, race and ethnicity (African Americans/Blacks, Hispanic Americans, American Indians, Alaska Natives, Native Hawaiians, and Native Pacific Islanders), disability, LGBTQ+, first generation college and socio-economic status.
Proposal: any time
The Sustained Availability of Biological Infrastructure program (SABI) supports the continued operation of extant infrastructure that will advance basic biological research. Infrastructure supported under this program may include cyberinfrastructure, instrumentation, experimental or observational facilities, biological living stocks which have ongoing costs of operation and maintenance that exceed the reasonable capacity of the host institution. Proposals must make a compelling case that sustained availability of the proposed infrastructure will advance or transform research in biological sciences as supported by the National Science Foundation. While other programs in the Division of Biological Infrastructure focus on research leading to future infrastructure or on the development or implementation of shared infrastructure, this program focuses on awards that ensure the continued availability of mature infrastructure resources critical to sustain the ability of today’s scientific community to conduct leading edge research. Awards made through this program are expected to lead to novel, impactful, and transformative science outcomes through research activities enabled by their use. Infrastructure that demonstrates substantial impact on research supported by the Directorate for Biological Sciences and its collaborating organizations is eligible for support under this program.
Proposal: any time
The Advanced Behavior and Life Prediction of Aerospace Materials Program shall advance state-of-the-art materials performance assessment and prediction capability by developing more efficient and comprehensive methods across modeling, simulation, and experimental domains on-site at AFRL/RXC located at Wright-Patterson Air Force Base. Research efforts shall range from proof-of-concept to technology development, demonstration, and transition.
Anticipated announcement: 5/2019
This Broad Agency Announcement (BAA) for the Foundational Science Research Unit (FSRU) of the U.S. Army Research Institute for the Behavioral and Social Sciences (ARI) solicits new proposals for its fiscal year 2019 program of basic research in behavioral science. In addition to looking for proposals that provide for programmatic efforts to develop and evaluate psychological and behavioral theory, we strongly encourage Applicants to propose novel, state-of-the-art, and multidisciplinary approaches that address difficult problems. A key consideration in the decision to support a research proposal is that its findings are likely to stimulate new, basic behavioral research which, in turn, will lead to improved performance of Army personnel and their units. ARI will not support proposals through this BAA that are primarily applied research projects (e.g., human factors studies or training program evaluations) or purely focused on physiology, psychopathology, or behavioral health. Collaboration is encouraged among institutions of higher education (IHE’s), non-profit organizations and commercial organizations.
White Paper: 4/12/2019
The objective of the Discovery Data Analysis Program (DDAP) is to enhance the scientific return of Discovery Program missions and broaden the scientific participation in the analysis of data, both recent and archived, collected by Discovery missions.
Step I: 8/29/2019
Step II: 11/1/2019
This Appendix seeks proposals on specific space technologies that are currently at low Technology Readiness Levels (TRL). Investment in innovative low-TRL research increases knowledge and capabilities in response to new questions and requirements, stimulates innovation, and allows more creative solutions to problems constrained by schedule and budget. Moreover, it is investment in fundamental research activities that has historically benefited the Nation on a broader basis, generating new industries and spin-off applications. This ECF Appendix seeks to tap into that talent base, challenging early career faculty to examine the theoretical feasibility of new ideas and approaches that are critical to making science, space travel, and exploration more effective, affordable, and sustainable.
The goal of this program is to support the advancement of regulatory science to facilitate the implementation and the assessment of continuous manufacturing and similar innovative monitoring and control techniques in the pharmaceutical sector. This will be accomplished by making awards to institutions of higher education and nonprofit organizations for the purpose of studying and recommending improvements to the process of continuous manufacturing of drugs and biological products (e.g., monoclonal antibodies and therapeutic enzymes). Examples of activities could include the development or modification of a novel manufacturing process (e.g., design, scale-up, and/or commercial scale), control method, and/or testing technology.
Applications to the FY 2019 Agriculture and Food Research Initiative - Sustainable Agricultural Systems (SAS) Request for Applications (RFA) must focus on approaches that promote transformational changes in the U.S. food and agriculture system within the next 25 years. NIFA seeks creative and visionary applications that take a systems approach, and that will significantly improve the supply of abundant, affordable, safe, nutritious, and accessible food, while providing sustainable opportunities for expansion of the bioeconomy through novel animal, crop, and forest products and supporting technologies. These approaches must demonstrate current and future social, behavioral, economic, health, and environmental impacts. Additionally, the outcomes of the work being proposed must result in societal benefits, including promotion of rural prosperity and enhancement of quality of life for those involved in food and agricultural value chains from production to utilization and consumption.
The U.S. Department of Transportation (DOT) Pipeline and Hazardous Materials Safety Administration (PHMSA) hereby requests applications from non-profit institutions of higher education for CAAP funding to research innovative solutions to pipeline corrosion and other known pipeline integrity challenges.