Emerging Models and Technologies for Computation (EMT)
See program guidelines for contact information.
Important Information for Proposers
ATTENTION: Proposers using the Collaborators and Other Affiliations template for more than 10 senior project personnel will encounter proposal print preview issues. Please see the Collaborators and Other Affiliations Information website for updated guidance.
A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 18-1), is effective for proposals submitted, or due, on or after January 29, 2018. Please be advised that, depending on the specified due date, the guidelines contained in NSF 18-1 may apply to proposals submitted in response to this funding opportunity.
The EMT program seeks to advance the fundamental capabilities of computer and information sciences and engineering by capitalizing on advances and insights from areas such as biological systems, quantum phenomena, nanoscale science and engineering, and other novel computing concepts. To bring fundamental changes to software, hardware and architectural design aspects of future computing models, collaborations among computer scientists, engineers, mathematicians, biologists and other disciplinary scientists are imperative.
Research of interest should move beyond evolutionary technological advances to innovations that enable fundamentally different ways of computing. These innovations should promise much higher speeds/chip densities or should solve more complex problems than traditional approaches currently permit.
The EMT program supports cross- and inter-disciplinary research and education projects that explore ideas, theory and experiments which go beyond conventional wisdom and venture into a range of uncharted territories in order to advance computing capabilities, and/or that produce innovative curricula or educational materials to help advance the training of new experts in emerging computing models and technologies. Explicit efforts will be made to support untested theories and approaches that provide plausible but high-risk opportunities. Proposals that are not clearly collaborative and/or interdisciplinary in nature are likely to be less competitive.