The Department of Energy (DOE) SC program in Advanced Scientific Computing Research (ASCR) is offering a grant for research applications focusing on utilizing artificial intelligence (AI) and machine learning (ML) to gain scientific insights from massive datasets produced by simulations, experiments, and observations. The grant aims to support high-impact approaches in data-intensive scientific analysis. The closing date for applications is May 27, 2021.
Opportunity ID: 332368
General Information
| Document Type: | Grants Notice |
| Funding Opportunity Number: | DE-FOA-0002493 |
| Funding Opportunity Title: | Data-Intensive Scientific Machine Learning and Analysis |
| Opportunity Category: | Discretionary |
| Opportunity Category Explanation: | – |
| Funding Instrument Type: | Cooperative Agreement |
| Category of Funding Activity: | Science and Technology and other Research and Development |
| Category Explanation: | – |
| Expected Number of Awards: | 7 |
| Assistance Listings: | 81.049 — Office of Science Financial Assistance Program |
| Cost Sharing or Matching Requirement: | No |
| Version: | Synopsis 1 |
| Posted Date: | Mar 25, 2021 |
| Last Updated Date: | Mar 25, 2021 |
| Original Closing Date for Applications: | May 27, 2021 |
| Current Closing Date for Applications: | May 27, 2021 |
| Archive Date: | Jun 26, 2021 |
| Estimated Total Program Funding: | $21,000,000 |
| Award Ceiling: | $800,000 |
| Award Floor: | $100,000 |
Eligibility
| Eligible Applicants: | Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled “Additional Information on Eligibility” |
| Additional Information on Eligibility: | All types of applicants are eligible to apply, except nonprofit organizations described in section 501(c)(4) of the Internal Revenue Code of 1986 that engaged in lobbying activities after December 31, 1995. Applicants that are not domestic organizations should be advised that: Individual applicants are unlikely to possess the skills, abilities, and resources to successfully accomplish the objectives of this FOA. Individual applicants are encouraged to address this concern in their applications and to demonstrate how they will accomplish the objectives of this FOA. Non-domestic applicants are advised that successful applications from non-domestic applicants include a detailed demonstration of how the applicant possesses skills, resources, and abilities that do not exist among potential domestic applicants. Applications that are submitted by applicants that have not submitted a required pre-application will be declined without further review. Federally-affiliated entities must adhere to the eligibility standards below: 1. DOE/NNSA National Laboratories DOE/NNSA National Laboratories are eligible to submit applications (either as a lead organization or as a team member in a multi-institutional team) under this FOA but may not be proposed as subrecipients under another organization’s application. If recommended for funding as a lead applicant, funding will be provided through the DOE Field-Work Proposal System. Additional instructions for securing authorization from the cognizant Contracting Officer are found in Section VIII of this FOA. 2. Non-DOE/NNSA FFRDCs Non-DOE/NNSA FFRDCs are not eligible to submit applications under this FOA but may be proposed as subrecipients under another organization’s application. If recommended for funding as a proposed subrecipient, the value of the proposed subaward may be removed from the prime 8 applicant’s award and may be provided through an Inter-Agency Award to the FFRDC’s sponsoring Federal Agency. Additional instructions for securing authorization from the cognizant Contracting Officer are found in Section VIII of this FOA. 3. Other Federal Agencies Other Federal Agencies are neither eligible to submit applications under this FOA nor to be proposed as subrecipients under another organization’s application. |
Additional Information
| Agency Name: | Office of Science |
| Description: |
The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in research applications to explore potentially high-impact approaches in the development and use of artificial intelligence (AI) and machine learning (ML) for scientific insights from massive data generated by simulation, experiments, and observations. |
| Link to Additional Information: | Funding Opportunity |
| Grantor Contact Information: | If you have difficulty accessing the full announcement electronically, please contact:
Steven Lee
Program Manager Phone 301-903-5710 Email:Steven.Lee@science.doe.gov |
Version History
| Version | Modification Description | Updated Date |
|---|---|---|
Related Documents
Folder 332368 Full Announcement-Machine Learning and Analysis -> DE-FOA-0002493.pdf
Packages
| Agency Contact Information: | Steven Lee Program Manager Phone 301-903-5710 Email: Steven.Lee@science.doe.gov |
| Who Can Apply: | Organization Applicants |
| Assistance Listing Number | Competition ID | Competition Title | Opportunity Package ID | Opening Date | Closing Date | Actions |
|---|---|---|---|---|---|---|
| 81.049 | DE-FOA-0002493 | Data-Intensive Scientific Machine Learning and Analysis | PKG00266156 | Mar 26, 2021 | May 27, 2021 | View |
Package 1
Mandatory forms
332368 RR_SF424_2_0-2.0.pdf
332368 RR_Budget_1_4-1.4.pdf
332368 PerformanceSite_2_0-2.0.pdf
332368 RR_OtherProjectInfo_1_4-1.4.pdf
Optional forms
332368 RR_SubawardBudget_1_4-1.4.pdf
332368 SFLLL_1_2-1.2.pdf
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