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Media Contacts
![Three staff members in Oak Ridge National Laboratory’s Fusion and Fission Energy and Science Directorate (FFESD) have moved into newly established roles facilitating communication and program management with sponsors of the directorate’s Nuclear Energy and Fuel Cycle Division.](/sites/default/files/styles/list_page_thumbnail/public/2024-02/3_people_spacing.jpg?h=08ef668f&itok=33PRJFyS)
Three staff members in ORNL’s Fusion and Fission Energy and Science Directorate have moved into newly established roles facilitating communication and program management with sponsors of the directorate’s Nuclear Energy and Fuel Cycle Division.
![Howard Wilson and Gary Staebler](/sites/default/files/styles/list_page_thumbnail/public/2023-12/Wilson-Staebler_0.png?h=ca9e32dd&itok=fLUb03Ia)
Two fusion energy leaders have joined ORNL in the Fusion and Fission Energy and Science Directorate, or FFESD.
![INFUSE logo](/sites/default/files/styles/list_page_thumbnail/public/2023-12/infuse_logo-011_0_0.jpeg?h=855b71fd&itok=vmDC02PO)
ORNL is leading three research collaborations with fusion industry partners through the Innovation Network for FUSion Energy, or INFUSE, program that will focus on resolving technical challenges and developing innovative solutions to make practical fusion energy a reality.
![Frontier, the fastest supercomputer in the world, provides expansive and energy-efficient power, which gives scientists the capability to train large AI models in a responsible way.](/sites/default/files/styles/list_page_thumbnail/public/2023-11/Frontier.jpg?h=c6980913&itok=Xugo8LTI)
ORNL is home to the world's fastest exascale supercomputer, Frontier, which was built in part to facilitate energy-efficient and scalable AI-based algorithms and simulations.
![Photo 1: Event organizers from the Nuclear Energy Fuel Cycle Division. Credit: Carol Morgan/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-10/2023-p15692.jpg?h=c6980913&itok=ommiWsy2)
The heat is on at this year’s Molten Salt Reactor Workshop – where top research and industry minds are melding to advance development on molten salt technology – at ORNL.
![Professional women in the IAEA’s Lise Meitner Programme 2023 cohort and supporters assembled at ORNL. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-10/2023-P14921.jpg?h=8f9cfe54&itok=EUxRbkj2)
The Department of Energy’s Oak Ridge National Laboratory hosted the second 2023 cohort of the International Atomic Energy Agency’s Lise Meitner Programme in October.
![: This schematic of tokamak core-pedestal-boundary regions show what will be simulated by an ORNL project applying machine learning to plasma physics modeling. Credit: Giacomin et al., J. Comput. Phys., 463, (2022) 111294, https://doi.org/10.1016/j.jcp.2022.11294](/sites/default/files/styles/list_page_thumbnail/public/2023-10/Fusion%20tokamak%20simulator.png?h=e1e3aba4&itok=kiVnri5A)
ORNL will lead three new DOE-funded projects designed to bring fusion energy to the grid on a rapid timescale.
![ORNL researchers are demonstrating an automation system for this portable system, currently based in Colorado, for treatment of non-traditional water sources to drinking water standards. Credit: Tzahi Cath/Colorado School of Mines](/sites/default/files/styles/list_page_thumbnail/public/2023-09/NAWI_comp01_0.jpg?h=d1cb525d&itok=I2fCHpSN)
Researchers at ORNL are developing advanced automation techniques for desalination and water treatment plants, enabling them to save energy while providing affordable drinking water to small, parched communities without high-quality water supplies.
![Cadet Elyse Wages, Mike Shaffer and Amanda Sandifer pose with a collected sample of air. Credit: Liz Neunsinger/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-09/20230627_140821.png?h=b6fd9b7a&itok=ESPeHBk1)
Cadet Elyse Wages, a rising junior at the United States Air Force Academy, visited ORNL with one goal in mind: collect air.
![ZEISS Head of Additive Manufacturing Technology Claus Hermannstaedter, left, and ORNL Interim Associate Laboratory Director for Energy Science and Technology Rick Raines sign a licensing agreement that allows ORNL’s machine-learning algorithm, Simurgh, to be used for rapid evaluations of 3D-printed components with industrial X-ray computed tomography, or CT. Using machine learning in CT scanning is expected to reduce the time and cost of inspections of 3D-printed parts by more than ten times.](/sites/default/files/styles/list_page_thumbnail/public/2023-08/ZEISS%20signing%20handshake_0.jpg?h=c6980913&itok=4J8nVrPc)
A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine