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Find similar grantsModeling of large macromolecular systems at extra-long time scales using AI is sponsored by National Science Foundation (NSF). This project focuses on modeling molecular systems using artificial intelligence to expand scientific knowledge of protein structures and their assembly in crowded, cell-like environments.
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Featured news and headlines | KU News KU Communicator Resources When Experts Attack! podcast $1. 5 million NSF grant will support KU research modeling molecular systems using AI LAWRENCE — University of Kansas researcher Ilya Vakser is leading a new National Science Foundation-funded project on modeling molecular systems using artificial intelligence.
Ilya Vakser Vakser, professor of molecular biosciences and director of the KU Computational Biology Program, will collaborate on the three-year, $1. 5 million grant with researchers at Stony Brook University and Stowers Institute.
By splitting the funding among these three teams, Vakser said, the project combines highly complementary areas of expertise on structure-based modeling of molecular interactions, theoretical foundation of reinforcement learning — a major direction in AI — and experimental approaches to protein aggregation.
The grant, titled “Modeling of large macromolecular systems at extra-long time scales using AI,” will allow Vakser and colleagues to expand scientific knowledge of protein structures and their assembly in the crowded, cell-like environments.
“This research is essential for scientists to better understand the work of the molecular machines that enable biological processes, allowing for the increased ability to modulate these processes when appropriate,” Vakser said. The research will generate the energy landscape of the macromolecular system as the foundation of the simulation approach, which samples the landscape in space and time, according to the research team.
The accuracy of the energy landscape will be significantly improved by application of the AI-based predictions. The modeling protocol will be radically advanced to a new AI-driven paradigm, according to the researchers.
Instead of explicitly simulating the entire trajectory of the system, the project will put forward AI methods that learn from the early segments of simulations and from the system parameters to model the long-term behavior. The emergent protein behavior will be investigated for the eye lens proteome. Crystallin protein oligomerization competes with aggregation, and how it is autonomously regulated is poorly understood.
The project puts forward a hypothesis that the lens crystallins likely operate through an entropic buffering mechanism. The aggregates promote their own growth by excluding volume that would otherwise be accessible to monomers, which creates an entropic drive for further aggregation.
The modeling approach, combined with wet-lab experiments, will provide a unique opportunity to test this hypothesis and to explore fundamental biology of protein homeostasis, according to the researchers. The grant also will involve graduate and undergraduate students in projects that apply AI to key problems in molecular biology.
“This education experience will allow for better career prospects in the current time of technological revolution,” Vakser said. He said the research will lead to the radical expansion of the existing approaches to modeling macromolecular processes in cells by incorporating the AI techniques.
“Thus the project will open up an uncharted territory for research, with unprecedented opportunities for life sciences that will have significant impacts on medicine, biotechnology, agriculture and beyond,” Vakser said.
According to the current listing, eligibility includes: Universities and research institutions (University of Kansas is leading a collaboration with Stony Brook University and Stowers Institute). Confirm the full requirements in the official notice before applying.
The current listing shows $1,500,000. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Modeling of large macromolecular systems at extra-long time scales using AI is funded by National Science Foundation (NSF). Verify program details on the funder's official page before applying.
This opportunity targets applicants in Kansas. If your organization operates elsewhere, check the official notice for location requirements.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
The EuroHPC Joint Undertaking launched its AI Gigafactories call on 30 July 2026 as a joint procurement, not a conventional research grant, and that distinction shapes who should respond. It seeks consortia or Special Purpose Vehicles bringing together companies, public entities, investors and other partners to establish up to seven AI Gigafactories across Europe - facilities that combine massive computing power and state-of-the-art AI processors with the software and cloud technology stacks, high-bandwidth connectivity and energy-efficient data centres needed to deliver frontier AI training, fine-tuning and inference at scale. This is the tier above the 19 AI Factories and 13 AI Factory Antennas EuroHPC already oversees, which serve SMEs and startups with free customised support; the Gigafactories are aimed at frontier-scale sovereign capacity. The financing model is blended: EuroHPC expects total private investment of more than EUR 20,000,000,000 across the EU, with successful projects receiving varying levels of EU and national co-funding calibrated to the size and range of sovereign services offered. Per-facility EU contributions have deliberately not been published, so applicants must build their own capital stack and negotiate co-funding rather than apply against a fixed grant envelope. Facilities may be established in a single Member State or span several, and cross-border developments are explicitly welcomed. The reference is EUROHPC-2026-CEI-AIGF-01 and the deadline is 12 November 2026 at 16:00 CET. Realistically this is a call for infrastructure-grade consortia with credible access to billions in capital, energy siting and hyperscale operations experience - not for individual research groups or early-stage startups, who should instead route through the existing AI Factories network.
Integrating Digital Public Goods to Advance AI-Enabled Formative Assessment (K-12 AI Infrastructure Program RFP) is sponsored by Digital Promise (with partners including Learning Data Insights, DrivenData, Massive Data Institute at Georgetown University, Catalyst @ Penn GSE). Funds development of openly licensed packages of tools, workflows, and documentation to integrate education-specific public goods (datasets, benchmarks, models) into AI-enabled formative assessment products for K-12 education.
This Air Force SBIR Direct to Phase II topic funds software that automatically converts technical data packages into executable robotic inspection paths, removing the manual waypoint programming that currently takes days for each new part. The system must ingest STEP and IGES 3D CAD, 2D drawings, bills of materials and PDF or text specifications, extract engineering intent such as GD&T tolerances, bind those tolerances to 3D geometry in a standardized data model, simulate the inspection virtually, and output collision-free commands for robotic manipulators from multiple vendors through a no-code operator interface with human-in-the-loop review. The target is to cut inspection programming time to under two hours and to reach TRL 6 to 7; building the physical manipulator is out of scope. Awards are up to 2 million US dollars over up to 24 months with optional assistance funding of up to 50,000 dollars. Because this is Direct to Phase II, proposals must document Phase I-type feasibility done largely outside SBIR or STTR funding and include quantitative baseline evidence such as parsing accuracy, architecture models and path-generation run-time benchmarks; proposals without it are ineligible. The topic opens in DSIP on 28 October 2026 and closes on 25 November 2026 at 12:00 p.m. Eastern, with a 35-page technical volume limit; the topic is ITAR/EAR controlled. Applicants should confirm the close date in DSIP, as some third-party listings show inconsistent status.
NSF 27-500 replaces the MRI solicitation: no per-institution limit, no tracks, $50 million for about 60 awards, and a January 4–29, 2027 window. What changes for research offices.
Read articleOn October 28, 2026, NSF sent its revised Guidance on Financial Assistance to OMB after 3,277 public responses. The revision drops four provisions borrowed from OMB's unfinished 2 CFR 200 rewrite: no fixed-price awards, no publication costs, termination for convenience, and indirect costs subject to available funds. The final 30-day comment period runs through about November 27. Here is what changed, what didn't, and what research offices should do before the December 11 funding cliff.
Read articleNSF 26-022, the July 8, 2026 Dear Colleague Letter last updated October 27, says NSF will prohibit its funds from supporting research collaborations with entities on U.S. restricted-party lists in FY2027, on the same timeline as the Guidance on Financial Assistance. Authorized organizational representatives and senior/key personnel will certify compliance at proposal submission. Here is what is covered, what is still undefined, and a 60-day plan for research offices and PIs.
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