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In late 2023, a long-trusted virtualization staple became the biggest open question on the enterprise IT roadmap.

Amid concerns of VMware licensing changes and steeper support costs, analysts noticed an exodus mentality. Forrester predicted that one in five large VMware customers would begin moving away from the platform in 2024. A subsequent Gartner community poll found that 74% of respondents were rethinking their VMware relationship in light of recent changes. CIOs contending with pricing hikes and product roadmap opacity face a daunting choice: double‑down on a familiar but costlier stack, or use the disruption to rethink how—and where—critical workloads should run.

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“There’s still a lot of uncertainty in the marketplace around VMware,” explains Matt Crognale, senior director, migrations and modernization at cloud modernization firm Effectual, adding that the VMware portfolio has been streamlined and refocused over the past

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U.S. National Science Foundation

Directorate for Technology, Innovation and Partnerships
     Translational Impacts

Full Proposal Deadline(s) (due by 5 p.m. submitting organization’s local time):

     Proposals Accepted Anytime

Table Of Contents

Summary of Program Requirements

Introduction Program Description Award Information Eligibility Information Proposal Preparation and Submission Instructions Proposal Preparation Instructions Budgetary Information Due Dates Research.gov/Grants.gov Requirements NSF Proposal Processing and Review Procedures Merit Review Principles and Criteria Review and Selection Process Award Administration Information Notification of the Award Award Conditions Reporting Requirements Agency Contacts Other Information Important Information And Revision Notes

Proposers must have either 1) received a prior award from NSF in a scientific or engineering field relevant to the proposed innovation that is currently active or that has been active within five years from the date of the NSF National I-Corps Teams proposal submission or 2) have participated

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A powerful new AI tool called Diag2Diag is revolutionizing fusion research by filling in missing plasma data with synthetic yet highly detailed information. Developed by Princeton scientists and international collaborators, this system uses sensor input to predict readings other diagnostics can’t capture, especially in the crucial plasma edge region where stability determines performance. By reducing reliance on bulky hardware, it promises to make future fusion reactors more compact, affordable, and reliable.

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On July 10, 2025, NSF issued an Important Notice providing updates to the agency’s research security policies, including a research security training requirement, Malign Foreign Talent Recruitment Program annual certification requirement, prohibition on Confucius institutes and an updated FFDR reporting and submission timeline.

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