FLAIR: Preparing Future Leaders in Artificial Intelligence and Machine Learning

FLAIR: Preparing Future Leaders in Artificial Intelligence and Machine Learning is sponsored by Department of Energy, RENEW. Supports HBCU faculty in preparing future leaders in AI and machine learning.

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gov Maintenance Calendar Scientific Machine Learning and Artificial Intelligence: Uncertainty Quantification Department of Energy - Office of Science Document Type:Grants Notice Funding Opportunity Number:DE-FOA-0002122 Funding Opportunity Title:Scientific Machine Learning and Artificial Intelligence: Uncertainty Quantification Opportunity Category:Discretionary Opportunity Category Explanation: Funding Instrument Type:Grant Category of Funding Activity:Science and Technology and other Research and Development Expected Number of Awards:6 Assistance Listings:81.

049 -- Office of Science Financial Assistance Program Cost Sharing or Matching Requirement:No Last Updated Date:Apr 16, 2019 Original Closing Date for Applications:May 31, 2019 Current Closing Date for Applications:May 31, 2019 Archive Date:Jun 30, 2019 Estimated Total Program Funding:$ 2,000,000 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 Federally Funded Research and Development Center (FFRDC) Contractors, and nonprofit organizations described in section 501(c)(4) of the Internal Revenue Code of 1986 that engaged in lobbying activities after December 31, 1995.

Other Federal agencies are not eligible to receive financial assistance awards and are therefore ineligible both to submit applications and to be proposed as subawardees. Other Federal agency’s FFRDCs are not eligible to submit applications or to be proposed as subawardees.

## Additional Information Agency Name:Office of Science Description:In support of the Executive Order on Maintaining American Leadership in Artificial Intelligence, the DOE Artificial Intelligence (AI) Program and DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announce their interest in the co-design of learning systems and AI environments that significantly advance the field of AI for public benefit within DOE’s Congressionally-authorized mission-space.

The principal focus of this FOA is on Uncertainty Quantification (UQ) for AI validation and prediction. Foundational research is needed for strengthening the mathematical and statistical basis of validating machine learning and AI predictions from data generated by the Office of Science’s user facilities and scientific simulations.

A critical open question for scientific machine learning (SciML) is: How do we make reliable predictions and uncertainty estimates from machine learning and AI models? Predictions can be greatly improved by including input uncertainties and insights from model discrepancies.

Research advances will be needed in methods that incorporate mathematical, statistical, scientific, and engineering principles for uncertainty estimates in extrapolative predictions. Furthermore, extensive literature in statistics can be leveraged for improving the model validation process. Advances in UQ will greatly enhance the mathematical and scientific computing foundations for accelerated research insights from SciML and AI.

Link to Additional Information:Office of Science, Advanced Scientific Computing Research Funding Opportunity Page Grantor Contact Information:If you have difficulty accessing the full announcement electronically, please contact: #### Health & Human Services * Frequently Asked Questions ## Your session will expire in 3 minutes. To continue working, click on the "OK" button below. This is being done to protect your privacy.

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