RSS feed source: National Science Foundation

Synopsis

The objective of the Data Science Corps program is to help build a strong national data science infrastructure and workforce. The Data Science Corps program seeks to engage data science students in real-world data science implementation projects. This engagement will help bridge the data-to-knowledge gap in organizations and communities at all levels, including local, state, and national, and will empower better use of data for more effective decision making. Data Science Corps participants will be able to sharpen their skills in data science by working on real-world projects focused on specific community needs, including rural communities, urban communities, academia, industry, or government. This partnership between communities and data scientists will serve the nation by helping produce a workforce-ready cohort of data scientists and technologists, who have experience with data science in action in real-world settings. The program welcomes proposals that seek to broaden

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RSS feed source: National Science Foundation

Synopsis

The Division of Mathematical Sciences (DMS) in the Directorate for Mathematical and Physical Sciences (MPS) at the National Science Foundation (NSF) and the Air Force Office of Scientific Research (AFOSR) plan to jointly support foundational mathematical and statistical research on Digital Twins in applied sciences. Recent years have witnessed a significant increase in the demand and interest in applications that involve collaborative teams developing and analyzing Digital Twins to support decision making in various fields, including science, engineering, medicine, urban planning, and more. Both agencies recognize the need to promote research aiming to stimulate an interplay between mathematics/statistics/computation and practical applications in the realm of Digital Twins.  This program encourages new collaborative efforts within the realm of Digital Twins, aiming at stimulating fundamental research innovation, pushing, and expanding the boundaries of knowledge, and exploring new frontiers in mathematics and computation for Digital Twin

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RSS feed source: National Science Foundation

Synopsis

The far-reaching impact and rate of innovation in the computer and information science and engineering fields has been remarkable, generating economic prosperity and enhancing the quality of life for people throughout the world.

More than a decade ago, the National Science Foundation’s (NSF) Directorate for Computer and Information Science and Engineering (CISE) established the Expeditions in Computing (Expeditions) program to build on past successes and provide the CISE research and education community with the opportunity to pursue ambitious, fundamental research agendas that promise to define the future of computing and information.

In planning Expeditions projects, investigators are strongly encouraged to come together within or across departments or institutions to combine their creative talents in the identification of compelling, transformative research agendas that look ahead by at least a decade and promise disruptive innovations in computer and information science and engineering for many years

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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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