Project Grant 2437949
- This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) is supporting a collaborative research effort between the Illinois Institute of Technology and other research partners to better understand how metals are utilized by cyanobacteria, one of the most abundant organisms on Earth. The $149,906 award will fund laboratory studies to compare artificial intelligence (AI) predictions about metal-binding in cyanobacteria proteins against experimental data...
- The National Science Foundation (NSF) awarded a $320,000 Project Grant under the Geosciences Program (CFDA 47.050) to the University of Delaware. The funding will support collaborative research to empower artificial intelligence (AI) to reveal phytoplankton community dynamics in coastal oceans. The project aims to address the scarcity of in-situ data for estuarine-coastal phytoplankton by constructing a large-scale database of phytoplankton observations, enabling global data sharing. It will...
- The National Science Foundation (NSF) awarded a $590,658 project grant to Arizona State University (ASU) under the Geosciences program (CFDA 47.050) to conduct research on the mechanisms of metal sulfide-enabled growth of anoxygenic photosynthetic bacteria. The research team will use transcriptomic sequencing, electron microscopy, and various chemical analysis tools to examine if all transition metal sulfide phases can enable the autotrophic growth of purple sulfur bacteria as sole sulfur and...
- The National Science Foundation (NSF) awarded a $899,999 Project Grant through its Geosciences (CFDA 47.050) program to the J. Craig Venter Institute (JCVI) on May 1, 2024. The grant will support collaborative research to characterize a novel blue copper ferritin protein in marine phytoplankton and explore how this protein may provide an ecological advantage in iron-limited ocean environments. The research aims to advance the understanding of how iron limitation impacts marine ecosystems and...
- This $586,557 federal Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) supports a collaborative research initiative to develop artificial intelligence (AI) models that can leverage gene sequence data to better understand ecosystem processes, with a focus on methane seep habitats. The project will collect new microbial samples from methane seeps off the coasts of Oregon and Washington and employ novel natural language processing AI approaches to predict...
- This Project Grant award for $529,636 from the National Science Foundation's Geosciences Program (CFDA 47.050) will enable the University of Louisiana at Lafayette to develop innovative artificial intelligence methods to analyze hyperspectral satellite imagery and construct a large-scale database to better understand phytoplankton community dynamics in coastal waters. The key products and services to be delivered include: Establishing a comprehensive database of phytoplankton observations to...
- This Project Grant award of $592,981 from the National Science Foundation's (NSF) Geosciences Program (CFDA 47.050) will support collaborative research at the University of California, Santa Cruz (UCSC) to develop advanced machine learning models for analyzing flow cytometry data and mapping the geographic distribution of ocean microbes. The project aims to combine large datasets of flow cytometry measurements with novel neural network models to reveal patterns in the geographical distribution...
- This $300,785 federal Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) supports a research project to characterize the chemical composition and bioavailability of dissolved organic matter released by marine nitrifying microorganisms. The project aims to determine how this material contributes to carbon cycling in the deep ocean, which may help improve biogeochemical models and inform potential marine carbon dioxide removal activities. The research will...
- The National Science Foundation (NSF) awarded a $441,467 Project Grant under the Geosciences program (CFDA 47.050) to the Trustees of the Colorado School of Mines (CSM) to develop a new artificial intelligence (AI) framework for analyzing hyperspectral data to map ore deposits. This 3-year project, starting on Sep 1, 2025, aims to improve the effectiveness of hyperspectral mineral mapping to accelerate the identification of critical mineral resources, enhance the nation's economic...
- This National Science Foundation (NSF) Geosciences Program (CFDA 47.050) grant award provides $593,864 to the New Jersey Institute of Technology (NJIT) from September 2024 to August 2027. The project will develop an AI-driven framework called SolarDM to generate high-resolution, high-cadence vector magnetograms of the solar chromosphere and photosphere. These AI-generated products will provide valuable data on the Sun's magnetic field to significantly advance the understanding and prediction...
This Project Grant award from the National Science Foundation's (NSF) Geosciences Program (CFDA 47.050) is supporting collaborative research at Rutgers, The State University to better understand how metals are utilized by cyanobacteria, one of the most abundant organisms in Earth's history. The $249,960 project is using artificial intelligence (AI) tools like AlphaFold to predict how proteins bind to metals within cyanobacterial cells. The research team is then testing these AI predictions through laboratory experiments growing cyanobacteria and analyzing the metal speciation using advanced X-ray techniques. By comparing the AI predictions to real-world data, the project aims to gain a better understanding of how metals cycle through aquatic ecosystems when cyanobacteria die and decompose. The award also includes an outreach component to engage schools and communities, including science activities for children, writing contests, and undergraduate research opportunities.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $250.0k | 7/16/25 |