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.
NIH Back Pain Consortium Research Program (BACPAC) |
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
Synthetic Biology for Engineering Applications (R01)
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.
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.
BD2K FOA: RFA-LM-17-001 “Big Data to Knowledge (BD2K) Enhancing the Efficiency and Effectiveness of Digital Curation for Biomedical Big Data (U01)”. NIH wishes to accelerate the availability and re-use of biomedical research data by transforming the processes used to curate digital data assets that are the product of biomedical research. Applications due December 15, 2016. Please share this opportunity with your interested scientific communities.
