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NOAA-OAR-CPO-2027-33392
Simpler.Grants.gov

Climate Program Office FY 2027 - Super Intelligence/Machine Learning Innovation to Advance NOAA's Earth System Science and Support Hazard Resilience

DOC NOAA - ERA Production (DOC-DOCNOAAERA)

$1 - $750,000
Due Feb 9, 2027
At a glance
Complex

This grant is for projects that use advanced artificial intelligence and machine learning to improve NOAA's understanding and prediction of Earth's systems and to help communities become more resilient to natural hazards like extreme weather and heat. The goal is to support innovative research that can lead to better forecasts, warnings, and preparedness strategies.

Who it's for: This grant is aimed at organizations and researchers who are working on cutting-edge AI and machine learning projects that can enhance NOAA's Earth system science capabilities. It's particularly suitable for those focusing on climate science, hazard resilience, and extreme weather predictions.

More details

Likely Disqualifiers

  • Proposals not focused on Super Intelligence/Machine Learning
  • Projects not aligned with NOAA's Earth system science
  • Applications exceeding the $750,000 funding limit
  • Incomplete or late submissions
  • Proposals not addressing one of the three priority areas

What You May Need

  • Detailed project proposal
  • Budget plan within $1 - $750,000
  • Evidence of expertise in AI and machine learning
  • Past research experience in Earth system science
  • Partnerships or collaborations with relevant organizations
  • Registration with federal grant systems
  • Project timeline and milestones
  • Impact assessment plan
  • Letters of support or collaboration

Cautions

  • Ensure alignment with NOAA's strategic goals
  • Focus on one of the specified priority areas
  • Prepare for a competitive application process
  • Adhere strictly to application guidelines
  • Consider potential environmental impacts

Generated from official source details for readability

Eligibility

Eligible Applicant Types

Other

Additional Criteria

Eligible applicants include organizations and researchers with expertise in artificial intelligence and machine learning, particularly those focused on climate science and hazard resilience. The grant is open to those who can contribute to NOAA's Earth system science capabilities and address one of the specified priority areas: multi-annual to decadal prediction, hazard resilience decision-support information, or extreme heat predictions and preparedness.

Overview

This grant is aimed at organizations and researchers who are working on cutting-edge AI and machine learning projects that can enhance NOAA's Earth system science capabilities. It's particularly suitable for those focusing on climate science, hazard resilience, and extreme weather predictions.

Likely Disqualifiers

  • Proposals not focused on Super Intelligence/Machine Learning
  • Projects not aligned with NOAA's Earth system science
  • Applications exceeding the $750,000 funding limit
  • Incomplete or late submissions
  • Proposals not addressing one of the three priority areas

Use of Funds

Funds can be used for research and development related to AI and machine learning applications in Earth system science, including personnel costs, equipment, data collection, and analysis. The focus should be on improving predictions and preparedness for natural hazards.

Cost Sharing

Not Required

Important Dates

Posted
Oct 27, 2026
Deadline
Feb 9, 2027(100 days)
Last Updated
Oct 28, 2026
Est. Award Date (AI estimate)
Spring 2027

Application Checklist

0/9 complete
  • Register with federal grant systems
  • Draft a detailed project proposal
  • Prepare a comprehensive budget plan
  • Gather evidence of relevant expertise
  • Establish partnerships or collaborations
  • Outline a clear project timeline
  • Conduct an impact assessment
  • Secure letters of support
  • Submit application by February 9, 2027

Award Range

$1 - $750,000

Deadline

Feb 9, 2027

Official Source

Always review the official grant listing for the most accurate and up-to-date information.

View Official Listing

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