Opportunity Information: Apply for DE FOA 0002321
The Department of Energy (DOE) Office of Science, through its Advanced Scientific Computing Research (ASCR) program, released a discretionary grant funding opportunity titled "Artificial Intelligence and Decision Support for Complex Systems" (Funding Opportunity Number DE-FOA-0002321; CFDA 81.049). The focus is on research and development in science and technology that pushes forward how artificial intelligence (AI) and machine learning (ML) can be developed and used to support computational decision-making in complex systems. In practical terms, the DOE is looking for potentially high-impact research ideas that connect modern AI/ML methods with decision support workflows where the system being analyzed is complicated, data-rich, and difficult to predict or control with traditional approaches alone.
The heart of the opportunity is decision support for complex systems, meaning proposals should center on methods that help people or automated systems make better choices under uncertainty and complexity, using computation as the backbone. While the announcement text provided is brief, the theme strongly implies interest in work that goes beyond training a model for accuracy and instead emphasizes how AI/ML can meaningfully inform decisions, such as selecting actions, evaluating tradeoffs, prioritizing resources, or improving outcomes when the underlying system has many interacting parts. This may include, for example, approaches that integrate simulation with learning, methods that quantify and communicate uncertainty, strategies for working with limited or noisy observations, or frameworks that make AI outputs usable within real decision processes for complex scientific or engineered systems.
Awards under this opportunity are issued as grants, with an award ceiling of $400,000 per award. DOE anticipated making around 20 awards, indicating a program-scale effort to seed multiple research directions rather than a single large center award. The opportunity was created on April 8, 2020, and originally closed on June 5, 2020, so it reflects a specific solicitation window, but it also signals ASCR's broader interest area: advancing AI/ML as part of the computational science toolkit used for high-consequence or high-complexity decision environments.
Eligibility is described as unrestricted, meaning it is open to any type of entity, with the standard caveat that applicants should review any additional eligibility clarifications in the full announcement. The sponsoring agency is the DOE Office of Science, and specifically ASCR, which typically supports foundational and applied research in advanced computing, algorithms, software, and related computational methods that align with DOE's scientific mission. Overall, this funding opportunity can be understood as an invitation for researchers to propose innovative AI/ML-driven computational approaches that improve decision support in settings where complexity, scale, and uncertainty make decisions difficult and where high-performance or advanced scientific computing methods are often essential.Apply for DE FOA 0002321
- The Department of Energy - Office of Science, Office of Science in the science and technology and other research and development sector is offering a public funding opportunity titled "Artificial Intelligence and Decision Support for Complex Systems" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 81.049.
- This funding opportunity was created on Apr 08, 2020.
- Applicants must submit their applications by Jun 05, 2020. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $400,000.00 in funding.
- The number of recipients for this funding is limited to 20 candidate(s).
- Eligible applicants include: Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled Additional Information on Eligibility.
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