RSS feed source: National Science Foundation

Synopsis

The economic prosperity and national security of the United States is reliant upon the nation’s capacity to remain globally competitive in the technological and computational fields. The nation’s competitiveness, however, is contingent upon its capacity to educate the next generation. Learning and teaching must be reimagined to better represent the diverse composition and perspective of our nation’s people and be expanded to encompass all pathways for students to receive a high-quality STEM education. A highly proficient and diverse technological and computational STEM workforce is needed to advance new discoveries in science, engineering, and technology in the service of the nation. The ITEST program is one way NSF is responding to the challenge and opportunity to provide all students with equitable access to a STEM education related to the technical and scientific workforce.

ITEST is an applied research and development program with goals to

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RSS feed source: National Science Foundation

Synopsis

Science of Learning and Augmented Intelligence (SL) supports potentially transformative research that develops basic theoretical insights and fundamental knowledge about principles, processes and mechanisms of learning, and about augmented intelligence — how human cognitive function can be augmented through interactions with others or with technology, or through variations in context. 

The program supports research addressing learning in individuals and in groups, across a wide range of domains at one or more levels of analysis, including molecular and cellular mechanisms; brain systems; cognitive, affective and behavioral processes; and social and cultural influences. 

The program also supports research on augmented intelligence that clearly articulates principled ways in which human approaches to learning and related processes, such as in design, complex decision-making and problem-solving, can be improved through interactions with others or through the use of artificial intelligence in technology. These could include ways of using knowledge

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RSS feed source: National Science Foundation

Synopsis

The primary aim of the Mathematical Sciences Infrastructure Program is to foster the continuing health of the mathematical sciences research community as a whole. In addition, the program complements the Workforce Program in the Mathematical Sciences in its goal to increase the number of well-prepared U.S. based individuals who successfully pursue careers in the mathematical sciences and in other professions in which expertise in the mathematical sciences plays an increasingly important role. The DMS Infrastructure program invites projects that support core research in the mathematical sciences, including: 1) novel projects supporting research infrastructure across the mathematical sciences community; 2) training projects complementing the Workforce Program, and 3) conference, workshop, and travel support requests that include cross-disciplinary activities or have an impact at the national scale. 

Proposals under this solicitation submitted to DMS Infrastructure must show engagement in developing or enhancing the mathematical sciences research

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RSS feed source: National Science Foundation

See Section V (Full Proposal Preparation Instructions) of this solicitation for additional information about the allowable maximum annual budget for years one through ten of each award.

Each NSF Engine can receive funding for up to 10 years. The initial two years of funding will support a ramp-up period. Continued support for an NSF Engine will be contingent upon its overall performance, including meeting its annual performance goals.

During the ramp-up period, an NSF Engine can be funded for a total of $15,000,000 over two years. The total amount of an NSF Engine award will not exceed $160,000,000 from NSF (over a period not exceeding ten years).

Beyond Year 1, NSF will annually conduct a comprehensive assessment of the NSF Engine’s performance, which will inform subsequent-year funding. A determination that the NSF Engine has failed to perform during an NSF annual review may result in

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