Category: Nuclear Engineering & Radiological Sciences
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New energy for fusion power
Annie Kritcher (BS NERS ‘05) launched a new era in fusion energy research.
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$19.4M for an ‘AI oracle’ to solve complex physics problems
U-Michigan leads new DOE-funded computational center focused on next-generation hypersonic flight.
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Global leadership in facilitating nuclear deployment and dialogue
Beyond research, Michigan Engineering is engaging around the world and at home to promote safe, effective adoption of nuclear energy.
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Michigan Plasma Prize honors Lawrence Berkeley Lab physicist
Eric Esarey, Michigan Engineering alumnus and leader in laser-plasma accelerators, receives 2025 award.
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A dual ion beam tests new steel under fusion energy-producing conditions
Researchers establish long-term helium trapping and swelling by titanium-carbide nanoparticles in a novel RAFM steel.
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Reinforcement learning for nuclear microreactor control
A machine learning approach outcompetes the industry standard for adjusting power generation to meet demand, especially in imperfect conditions.
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The US has a new most powerful laser
Hitting 2 petawatts, the NSF-funded ZEUS facility at U-M enables research that could improve medicine, national security, materials science and more.
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Nuclear fuels and materials research earns Todd Allen national award
American Nuclear Society recognizes Allen for “outstanding” nuclear fuels and materials R&D.
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Using artificial intelligence to help students learn about socially engaged design
While nuclear power can reap enormous benefits, it also comes with some risks.
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Hands-on reactor research experience for students
Collaboration with Ohio State strengthens ties between institutions working to bolster U.S. nuclear expertise
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Materials for fusion reactors: U-M launches five new projects
‘Understanding the behavior of materials under extreme conditions is key to developing fusion reactors.’
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Tool creates transparent, automated machine learning models for nuclear energy
A new tool simplifies machine learning development for nuclear engineers while incorporating model explainability features to help improve transparency.