NSF-NIST Scholars in Residence
NSF-NIST Scholars in Residence supports students, postdoctoral fellows, faculty, and industry professionals in NIST residencies to develop biotechnology standards and accelerate engineering biology research.
This grant was recently updated
Full proposals are now due before April 1 in a given fiscal year, replacing the April 1, 2026 cutoff for FY2026 consideration. Invited applicants must submit through the Core Research in Biological Sciences solicitation rather than the Molecular and Cellular Biosciences Program; David Rockcliffe was removed as an NSF contact.
Funder: U.S. National Science Foundation
Due Dates: February 1, 2027 (NIST white paper) | April 1, 2027 (NSF full proposal)
Funding Amounts: Faculty/PI: up to 6 months’ salary and benefits; postdoctoral fellows and graduate students: up to 1 year, plus specified relocation support
Summary: Supports residencies at NIST to develop bioeconomy standards, including standardized protocols, calibration tools, and AI-ready data.
Key Information: Full proposals require a NIST invitation and must be submitted by an NSF-eligible principal investigator.
Description
The NSF-NIST Scholars in Residence initiative brings graduate students, postdoctoral fellows, faculty members, and industry professionals into NIST laboratories to develop expertise in biotechnology standards and foster their adoption. Jointly supported by the U.S. National Science Foundation and the National Institute of Standards and Technology, the initiative aims to advance bioeconomy standards and metrics that enable commerce.
Projects focus on broadly applicable workflows for engineering microorganisms, using integrated laboratory automation at NIST’s Living Measurement Systems Foundry and mentoring from NIST hosts. Priorities include machine-readable code for standardized protocols, biological or physical calibration and benchmarking objects, and AI-ready data standardization and integration. Scholars and NIST may jointly identify other objectives. The work aims to accelerate engineering biology research and improve data for predicting function from sequences.
