Data Analysis & Modeling Support

The contract seeks to develop advanced algorithms and statistical models for interpreting particulate speciation data, leveraging machine learning techniques and environmental correlation modeling to extract meaningful insights from complex environmental datasets. This effort is critical for enhancing the understanding of airborne particulate composition and its implications, particularly in defense and public health contexts, with an emphasis on predictive analytics and data-driven decision support. The work will require expertise in data science, environmental science, and computational modeling to build robust systems capable of identifying patterns and relationships between particulate matter and environmental variables. This is a small business set-aside subcontract under the SBA’s SBIR/STTR mandate, restricted to organizations with fewer than 500 employees, and falls under the NAICS code 541690 for other scientific and technical consulting services. It is sponsored by the Defense Health Agency within the U.S. Department of Defense, reflecting a focus on applications that support military and veteran health outcomes. Proposals must be submitted by July 8, 2026, and the project is posted on the DOD SBIR/STTR portal, indicating a connection to innovation-driven defense research priorities. The performance location and point of contact details are unspecified, suggesting flexibility in execution but also a need for proposers to clarify operational logistics.