Funding
Opportunities

The aim of the Simons Collaborations in MPS program is to stimulate progress on fundamental scientific questions of major importance in mathematics, theoretical physics and theoretical computer science.

The program is intended to support high-risk theoretical mathematics, physics and computer science projects of exceptional promise and scientific importance on a case-by-case basis.

The Simons Fellows program in Mathematics provides funds to faculty for up to a semester-long research leave from classroom teaching and administrative obligations.

The program is intended to support established institutes or centers in the mathematics and physical sciences through funding to help strengthen contacts within the international scientific community.

The goal of the program is to support the “mathematical marketplace” by substantially increasing collaborative contacts between mathematicians. The foundation will make a large number of collaboration grants to accomplished, active researchers in the United States who do not otherwise have access to funding that allows support for travel and visitors.

The National Science Foundation Directorates for Mathematical and Physical Sciences (NSF/MPS) and for Biological Sciences (NSF/BIO) and the Simons Foundation Division of Mathematics and Physical Sciences (SF/MPS) shall jointly sponsor a new research institute to facilitate collaborations among groups of mathematicians (including statisticians and computational scientists) and biologists.

Each Simons Symposia series brings together mathematicians, theoretical physicists and/or theoretical computer scientists to interact and collaborate in a series of up to three symposia, held every second year and focusing on one topic or a tightly connected group of topics.

The NSF-Simons Research Collaborations on the MoDL initiative awarded two collaborations designed to support research activities focused on a particular set of topics involving some of the most challenging questions in the general area of mathematical and scientific foundations of deep learning.