RSS feed source: National Science Foundation

The U.S. National Science Foundation, in partnership with Capital One and Intel, today announced a $100 million investment to support five National Artificial Intelligence Research Institutes and a central community hub. These institutes will drive breakthroughs in high-impact areas such as mental health, materials discovery, science, technology, engineering and mathematics education, human-AI collaboration and drug development.

This public-private investment aligns with the White House AI Action Plan, a national initiative to sustain and enhance America’s global AI dominance.

“Artificial intelligence is key to strengthening our workforce and boosting U.S. competitiveness,” said Brian Stone, performing the duties of the NSF director. “Through the National AI Research Institutes, we are turning cutting-edge ideas and research into real-world solutions and preparing Americans to lead in the technologies and jobs of the future.”

While headlines often focus on the newest chatbot, AI is quietly powering advances across nearly every sector, helping doctors detect diseases, enabling smarter manufacturing and supporting resilient agriculture and financial security. The AI Institutes are designed to translate cutting-edge research into scalable, practical solutions that improve lives.

The institutes will also help build a national infrastructure for AI education and workforce development, training the next generation of researchers and practitioners, empowering educators and reaching into communities.

This effort directly supports the goals outlined in Executive Order 14277, “Advancing Artificial Intelligence Education for American Youth,” which calls for expanding

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RSS feed source: National Science Foundation

In-depth analysis

July 29, 2025

In our Annual Energy Outlook 2025 (AEO2025), we project regional differences in natural gas markets will encourage increased natural gas flows from the mid-Atlantic to the southern Gulf Coast in the coming decades. Across the cases we explored, we project production from the Appalachian Basin in the mid-Atlantic and Ohio region will increasingly meet growing demand on the Gulf Coast in the South Central region, driven largely by increasing liquefied natural gas (LNG) exports. The economics of increased production in the Appalachian Basin are more favorable by 2030, and our model shows natural gas transiting through the Eastern Midwest region on the way to the Gulf Coast.

We froze assumptions for AEO2025 in December 2024, and we did not include market changes, recently passed legislation, regulations, executive actions, or court rulings after

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Job ID: 260401

American University of Sharjah Postdoctoral Fellowship Award, PDFA26
American University of Sharjah Job Categories Post-Doc
Academic Fields Water Resources Engineering
Transportation Engineering
Sustainable Engineering
Structural Engineering
Polymer Science
Ocean Engineering
Mechatronics
Mechanical Engineering
Material/Metallurgy
Manufacturing & Quality Engineering
Industrial & Systems Engineering
Geotechnical
Ecological and Environmental
Engineering Physics
Engineering Mechanics
Electrical and/or Electronics
Computer Engineering
Computer Science
Construction Engineering/Management
Civil Engineering
Chemical/Petroleum
Bioengineering (all Bio-related fields)
Engineering – Other

Dear Applicants,

We are pleased to announce the launch of a new cycle of the AUS Postdoctoral Fellowship Award, PDFA26. 

The fellowship is designed to attract outstanding researchers worldwide to contribute to the growing and thriving scholarly environment at the American University of Sharjah (AUS). 

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RSS feed source: National Science Foundation

Researchers are exploring AI-powered digital twins as a game-changing tool to accelerate the clean energy transition. These digital models simulate and optimize real-world energy systems like wind, solar, geothermal, hydro, and biomass. But while they hold immense promise for improving efficiency and sustainability, the technology is still riddled with challenges—from environmental variability and degraded equipment modeling to data scarcity and complex biological processes.

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