NSF invests in promising innovations and associated talent pipelines for national security
The U.S. National Science Foundation announced a pilot to accelerate the translation of emerging technologies for national security applications. This pilot will identify and advance promising research and develop associated talent pipelines, with a focus on specific regions of the country.
The multiyear initiative will be led by LMI (NSF Award # 2530262), doing business as NobleReach Foundation, through its Science to Venture platform and will engage two NSF Innovation I-Corps (NSF I-Corps™) Hubs. The hubs will be structured around four pillars: commercialization assistance, local capability building, talent network development and capital catalyzation. NSF will invest in end-to-end support spanning rigorous portfolio analysis and tailored roadmaps for high-potential university technologies; train-the-trainer curricula and toolkits that institutionalize proven commercialization methods; pathways linking entrepreneurs, mentors and subject-matter experts to research teams; and a blended capital strategy that crowds in philanthropic, public and private investments to de-risk early translation stages.
Over two years, an estimated 12 to 20 research projects relevant to national security will be shepherded through milestone-based development plans, yielding replicable frameworks and artificial intelligence-enabled assets that can be extended across the lab-to-market ecosystem.
"With this effort, we can accelerate the translation of NSF's investments in basic research to commercially-promising products that drive local economic growth and improve national security," said Erwin Gianchandani, NSF assistant director for Technology, Innovation and Partnerships.
The pilot will implement proven technology translation methodologies while building sustainable local capacity for innovation. It will apply tested frameworks for research commercialization, incorporating best practices, enabling consistent evaluation of commercialization potential while maintaining flexibility across technology types and regional contexts.
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