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NSF calls for research in real-time learning and decision-making for engineered systems

April 12, 2018

Real-time learning and decision-making in engineering systems will play an increasingly critical role in modern engineering systems and infrastructures, such as the smart grid, transportation and mobility, water distribution systems, healthcare logistics and delivery systems, advanced manufacturing, chemical and biological process systems, sensor networks, sustainable buildings, smart and connected communities, and dynamic control of transport processes.

To lead the way toward safe, reliable, and efficient data-enabled engineering systems, the NSF Directorate for Engineering calls for research proposals in fundamental theory, algorithms, engineering principles, and applications for real-time learning and decision-making.

Learn more in Dear Colleague Letter: Real-Time Learning and Decision-Making in Engineered Systems (Real-D) (NSF 18-063).

The National Science Foundation (NSF) is an independent federal agency that supports fundamental research and education across all fields of science and engineering. In fiscal year (FY) 2019, its budget is $8.1 billion. NSF funds reach all 50 states through grants to nearly 2,000 colleges, universities and other institutions. Each year, NSF receives more than 50,000 competitive proposals for funding and makes about 12,000 new funding awards.

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