National Institute of Health

Application of Artificial Intelligence and Machine Learning for Advancing Environmental Health Sciences

These Funding Opportunity Announcements (FOA) solicits Phase I (R43) SBIR  and Phase I (R41) STTR grant applications from small business concerns (SBCs) to develop promising methodologies using Artificial Intelligence (AI) and Machine Learning (ML) approaches to advance environmental health research and decisions.

Joint DMS/NIGMS Initiative to Support Research at the Interface of the Biological and Mathematical Sciences (DMS/NIGMS)

The Division of Mathematical Sciences (DMS) in the Directorate for Mathematical and Physical Sciences (MPS) at the National Science Foundation (NSF) and the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health (NIH) plan to support research in mathematics and statistics on questions in the biological and biomedical sciences. Both agencies recognize the need for promoting research at the interface between the mathematical sciences and the life sciences. This program is designed to encourage new collaborations, as well as to support existing ones

NIH Data Commons Pilot Phase OT3

The purpose of the announcement is to invite applications from applicants who have an interest in performing high impact, cutting-edge scientific and computing activities necessary to establish an NIH Data Commons. The goal of the NIH Data Commons is to accelerate new biomedical discoveries by providing a cloud-based platform where investigators can store, share, access, and compute on digital objects (data, software, etc.) generated from biomedical research and perform novel scientific research including hypothesis generation, discovery, and validation.

Modeling of Infectious Disease Agent Study Research Projects (R01)

The purpose of this funding opportunity announcement (FOA) is to support innovative research that will develop and apply computational tools and methods for modeling interactions between infectious agents and their hosts, disease spread, prediction systems and response strategies. The models should be useful to researchers, policymakers, or public health workers who want to better understand and respond to infectious diseases.