Foundations for Digital Twins as Catalyzers of Biomedical Technological Innovation
This grant funds interdisciplinary research developing foundational methods for digital twins contributing to biomedical technological innovation.
Browse similar grants
This grant is no longer accepting proposals
U.S. National Science Foundation has archived this opportunity.
Funder: U.S. National Science Foundation
Due Dates: May 5, 2025 | First Monday in May, Annually Thereafter
Funding Amounts: $4–5M total per year; up to 10 awards; project duration up to 3 years; max $1M per collaborative project
Summary: Supports interdisciplinary research advancing foundational methods and algorithms for digital twins and synthetic data in biomedical technology and healthcare.
Key Information: Proposals must include at least two senior/key personnel from both mathematical sciences and a relevant domain (biomedical sciences or computer science with cyberinfrastructure expertise).
Description
This program supports inherently interdisciplinary research projects that advance the mathematical, statistical, and engineering foundations behind the development and use of digital twins and synthetic data in biomedical and healthcare applications. The initiative especially encourages work on digital, in silico models for the evaluation of medical devices, with the broader goal of catalyzing biomedical technological innovation. Projects should address foundational challenges such as computational representations of physiological systems, verification and validation, uncertainty quantification, transferability and generalizability, ethics and privacy, and mechanisms for model validation and sharing.
The program is a collaboration between NSF, NIH, and FDA, and aims to accelerate innovations in biomedical technologies through the principled development of digital twins and synthetic human models. Projects may leverage virtual representations at multiple scales (from single systems to populations), and should consider regulatory relevance, ethical, legal, and social implications (ELSI), and broad dissemination of tools and methods.
