Computational and Data Enabled Science and Engineering in Mathematical and Statistical Sciences | Apply Online

Opportunity Summary:

The National Science Foundation (NSF) grant opportunity titled "Computational and Data Enabled Science and Engineering in Mathematical and Statistical Sciences" (often referred to as CDS and E MSS) supports fundamental research in mathematics and statistics that directly targets challenges created by modern computation and the rapid growth of data. The program is built around the idea that scientific and engineering progress increasingly depends on large scale computational modeling and simulation, alongside massive volumes of digital, experimental, and observational data. As these trends accelerate, they create new mathematical and statistical problems that cannot be solved well with existing theory alone. This opportunity is meant to push the field forward by funding work that develops the next generation of core concepts, methods, and tools needed for computation heavy and data intensive research.

The emphasis is not on applying off the shelf methods to a particular dataset or application, but on advancing mathematics or statistics in a substantial way while being motivated by computational or big data needs. In practice, the program is looking for projects that create new theory, new algorithms, new inferential frameworks, or new mathematical foundations that make it possible to model, simulate, analyze, or extract meaning from complex systems and very large datasets. The research is expected to be genuinely mathematical or statistical at its core, with a clear connection to computational or data enabled challenges that matter broadly across science and engineering.

NSF positions this program within its broader CDS and E enterprise, which is an agency wide effort to strengthen an emerging discipline at the intersection of computation, data, and the foundational sciences. Proposals are expected to include a Principal Investigator or co Principal Investigator who is an active researcher in mathematical or statistical sciences in an area supported by NSF's Division of Mathematical Sciences. The program also encourages proposals that bring in multidisciplinary collaborations, reflecting the reality that many of the hardest computation and data problems sit at the boundaries between math, statistics, computer science, and domain sciences. Another encouraged component is training: projects that help educate and prepare mathematicians and statisticians to work effectively in CDS and E contexts are viewed as aligned with the program's goals.

From an administrative and funding standpoint, this is a discretionary NSF grant program in the science and technology research and development category. The opportunity lists an estimated total funding level of about $5,000,000 and anticipates roughly 20 awards. Individual awards can range from a floor of $20,000 up to a ceiling of $1,000,000, and the solicitation indicates there is no cost sharing or matching requirement. Eligible applicants are described as unrestricted, meaning a wide range of entity types may apply, subject to any additional eligibility language in the full announcement. The program is associated with CFDA numbers 47.049 (Mathematical and Physical Sciences) and 47.070 (Computer and Information Science and Engineering). Historically, it noted co funding in fiscal year 2012 by the NSF Office of Cyberinfrastructure, signaling an interest in work that connects strongly to advanced computing and related infrastructure needs.

Key dates in the source information show earlier submission windows in January 2012 and late 2012, followed by annual windows thereafter, with a listed closing date of December 9, 2015 and an archive date of January 8, 2026. For applicants trying to locate full details or troubleshoot access, the notice points to NSF grants.gov support (grantsgovsupport@nsf.gov) for technical issues accessing the announcement. Overall, the core message of the opportunity is straightforward: NSF is investing in deep, foundational mathematical and statistical research that is driven by, and designed to overcome, the new realities of large scale computation and the data explosion across modern science and engineering.

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