Machine Learning, Artificial Intelligence, and Data Resources for Fusion Energy Sciences
The DOE SC program in Fusion Energy Sciences (FES) hereby announces its interest in applications in the areas of Machine Learning (ML), Artificial Intelligence (AI), and Data Resources for fusion energy and plasma sciences.
The goal of this FOA is to support multi disciplinary teams aiming to apply advanced and autonomous algorithms to address high-priority research opportunities across the FES program.
Applicants are encouraged to propose research in new systems for managing, formatting, curating, and accessing experimental and simulation data, provided in publicly available databases.
Of high programmatic importance are approaches that support the realization of a fusion pilot plant on a decadal timescale.
The goal of this FOA is to support multi disciplinary teams aiming to apply advanced and autonomous algorithms to address high-priority research opportunities across the FES program.
Applicants are encouraged to propose research in new systems for managing, formatting, curating, and accessing experimental and simulation data, provided in publicly available databases.
Of high programmatic importance are approaches that support the realization of a fusion pilot plant on a decadal timescale.
Obtain Full Opportunity Text:
https://grants.nih.gov/grants/guide/rfa-files/RFA-CA-23-015.html
Additional Information of Eligibility:
Other Eligible Applicants include the following: Alaska Native and Native Hawaiian Serving Institutions; Asian American Native American Pacific Islander Serving Institutions (AANAPISISs); Eligible Agencies of the Federal Government; Faith-based or Community-based Organizations; Hispanic-serving Institutions; Historically Black Colleges and Universities (HBCUs); Indian/Native American Tribal Governments (Other than Federally Recognized); Non-domestic (non-U.S.) Entities (Foreign Organizations); Regional Organizations; Tribally Controlled Colleges and Universities (TCCUs) ; U. S. Territory or Possession.
Full Opportunity Web Address:
http://grants.nih.gov/grants/guide/rfa-files/RFA-CA-23-015.html
Contact:
Agency Email Description:
matthew.lanctot@science.doe.gov
Agency Email:
Date Posted:
2022-12-22
Application Due Date:
Archive Date:
2023-04-14
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