Enhancing ICE cloud retrieval through multitask machine learning

Ended Grant For Research Awarded Last action

Contract number
80NSSC24K1070
Awardee
Awarding agency
Obligated
$300,000

Award facts

Contract value 100% obligated
$300,000.00 Current and potential, with all options
Completion date 100% complete
Current and potential, with all options
NAICS code
Not on record
Set-aside
No set-aside
Competition
Full and Open Competition Basic Research
Pricing
Other
Place of performance
Washington, DC 20016, USA

Award details come from SAM.gov/FPDS.

Transactions

Action Obligated Date
Funding Only Action P00001 · The research objective of this project is to develop multitask machine learning techniques for analyzing and interpreting satellite passive microwave data to… $151,570
Original award $148,430

About this award

This is a $300,000.00 grant awarded by the National Aeronautics and Space Administration (NASA) Shared Services Center to American University, a private non-profit research university located in Washington, D.C. The grant is for a research project titled "Enhancing Ice Cloud Retrieval Through Multitask Machine Learning," which aims to develop advanced machine learning techniques to analyze and interpret satellite passive microwave data to retrieve information about ice clouds. The project has an ultimate completion date of April 29, 2026. American University has extensive experience in conducting scientific research and supporting government agencies through federal contracts and grants, particularly in areas such as astrophysics, solar physics, and computational research. This award does not have a set-aside designation.

Quick answers

Who was awarded contract 80NSSC24K1070?

80NSSC24K1070 was awarded to American University by Shared Services Center (National Aeronautics and Space Administration) on April 30, 2024.

How much is contract 80NSSC24K1070 worth?

The government has obligated $300,000 on 80NSSC24K1070.

Was contract 80NSSC24K1070 competed?

The extent of competition for 80NSSC24K1070 was full and open competition.

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