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Division Spotlight
Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
Meeting Spotlight
2025 ANS Annual Conference
June 15–18, 2025
Chicago, IL|Chicago Marriott Downtown
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Jun 2025
Jan 2025
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Nuclear Science and Engineering
July 2025
Nuclear Technology
Fusion Science and Technology
Latest News
Smarter waste strategies: Helping deliver on the promise of advanced nuclear
At COP28, held in Dubai in 2023, a clear consensus emerged: Nuclear energy must be a cornerstone of the global clean energy transition. With electricity demand projected to soar as we decarbonize not just power but also industry, transport, and heat, the case for new nuclear is compelling. More than 20 countries committed to tripling global nuclear capacity by 2050. In the United States alone, the Department of Energy forecasts that the country’s current nuclear capacity could more than triple, adding 200 GW of new nuclear to the existing 95 GW by mid-century.
13th Nuclear Plant Instrumentation, Control & Human-Machine Interface Technologies (NPIC&HMIT 2023)
Technical Session
Tuesday, July 18, 2023|10:00–11:45AM EDT|301C
Session Chair:
Xingang Zhao
Alternate Chair:
Daniel G. Cole
Session Organizer:
Brent D. Shumaker
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Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems
10:00–10:25AM EDT
Xingang Zhao (ORNL), Bryan Maldonado Puente (ORNL), Siyan Liu (ORNL), Seung-Hwan Lim (ORNL), William Gurecky (ORNL), Dan Lu (ORNL), Matthew Howell (ORNL), Frank Liu (ORNL), Wesley Williams (ORNL), Pradeep Ramuhalli (ORNL)
Paper
Quantifying Uncertainty of Deep Reinforcement Learning Based Decision Making for Operations and Maintenance of Nuclear Power Plant
10:25–10:50AM EDT
Ryan M. Spangler (Univ. Pittsburgh), Daniel G. Cole (Univ. Pittsburgh)
Development of a Virtual Sensor for Leading Edge Flow Meter Measurements in Nuclear Power Plants
10:50–11:15AM EDT
Brent Shumaker (Analysis and Measurement Services Corp.), Christopher Guillotte (Analysis and Measurement Services Corp.), Brian Moazen (Analysis and Measurement Services Corp.), Zachary Becker (Analysis and Measurement Services Corp.), Jamie Coble (Univ. Tennessee, Knoxville)
Presented by Ryan O’Hagan (Analysis and Measurement Services Corp.)
Using Machine Learning to Extract and Normalize Historic Maintenance Data from Work Descriptions
11:15–11:40AM EDT
Nicholas Zwiryk (Westinghouse Electric Co.), Steven Yurkovich (Westinghouse Electric Co.)
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