Omnipreneurship Awards for a Better Sustainable Global Ecosystem

Al-Dabbagh Group (ADG) and Petrolube Oil Company are working together to award grants for their solutions in minimizing overall resource use and the environmental impacts of company operations. The main goal of this challenge is to find a way to repurpose or biodegrade vegetable cooking oils to increase profits while simultaneously mitigating any negative environmental effects, creating a better global ecosystem. 

MAXIMUM AWARD AMOUNT

Phase I: Up to 5 Finalists to win $20,000 each
Phase II: One winner to win $1 Million.

OPEN TO

No restrictions

PERIOD OF PERFORMANCE

TBD

APPLICATION DUE

November 30th 2021, 11.59 pm CET

EXPECTED NOTIFICATION DATE

March 2022
June 2022

PROJECT START DATE

TBD

For more information, visit the grant page.

Bio-Manufacturing Omics Data Challenge For Promising Researchers

GlaxoSmithKline plc (GSK) is awarding grants to proposals who meet their needs within appropriate analysis of pharmaceutical bio-manufacturing and may fund future joint collaborations for the winners who show potential for GSK biomanufacturing or research. They are permitting third parties who are interested in this research to view these dataset to analyze. GSK’s goal with this project is to find promising researchers who will discover a fix to bio-manufacturing problems and come up with innovative solutions to analyzing large datasets, resulting in medical advances improving the lives of the general population.

MAXIMUM AWARD AMOUNT

Three winners to win 7000 Euro each

OPEN TO

Academic researchers, companies, research institutes, start-ups and individuals

PERIOD OF PERFORMANCE

TBD

APPLICATION DUE

October 14, 2021 at 5:00 p.m. EST

EXPECTED NOTIFICATION DATE

December 2021

PROJECT START DATE

TBD

For more information, visit the grant page.

Industry-University Cooperative Research Centers Program (IUCRC)

The National Science Foundation (NSF) is offering grants to science, engineering and education research projects that look to help advance the leadership rank of the U.S. in innovation and developing workforces within highly-skilled science and engineering careers. This opportunity will connect government agencies and industry innovators with academic research teams to achieve the shared goals of this project.

MAXIMUM AWARD AMOUNT

Planning Grants: $20,000
Phase I: $150,000 per year
Phase II: $100,000 per year
Phase II+: $150,000 per year
Phase III: $50,000 per year

OPEN TO

IHEs

PERIOD OF PERFORMANCE

Planning Grant: 12 months.
Phases I, II, II+, and III: 60 months.
Site addition: Center’s remaining grant duration in its current Phase.

APPLICATION DUE

  • 5 p.m. submitter’s local time
  • Preliminary:
    • Second Wednesday in March, Annually
    • September 08, 2021
    • Second Wednesday in September, Annually Thereafter
    • Preliminary proposals are required prior to submission of planning grants. Submitters seeking a waiver of the planning grant stage must submit the waiver request as a preliminary proposal.
  • Full Proposal:
    • Second Wednesday in June, Annually
    • December 08, 2021
    • Second Wednesday in December, Annually Thereafter

EXPECTED NOTIFICATION DATE

TBD

PROJECT START DATE

TBD

For more information, visit the grant page.

Ecology and Evolution of Infectious Diseases Program Funding New Projects

The National Science Foundation (NSF) is funding this program in hopes to better understand how changes within our ecological, behavioral, physiological, and other systems have impacted transmission of infectious diseases.

MAXIMUM AWARD AMOUNT

 $3,000,000

OPEN TO

“The categories of proposers eligible to submit proposals to the National Science Foundation are identified in the NSF Proposal & Award Policies & Procedures Guide (PAPPG), Chapter I.E. Unaffiliated individuals are not eligible to submit proposals in response to this solicitation”

PERIOD OF PERFORMANCE

Maximum of 5 years

APPLICATION DUE

November 24, 2021;
November 16, 2022;
Third Wednesday in November, Annually Thereafter.
5 p.m. submitter’s local time

EXPECTED NOTIFICATION DATE

TBD

PROJECT START DATE

TBD

For more information regarding this grant, visit the NSF website.

FAIR Data and Models for Artificial Intelligence and Machine Learning

The Department of Energy Office of Science (SC) program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in making research data and artificial intelligence (AI) models findable, accessible, interoperable, and reusable (FAIR1) to facilitate the development of new AI applications in SC’s congressionally authorized mission space, which includes the advancement of AI research and development. In particular, ASCR is interested in supporting FAIR benchmark data for AI; and FAIR frameworks for relating data and AI models

For this FOA, AI is inclusive of, for example, machine learning (ML), deep learning (DL), neural networks (NN), computer vision, and natural language processing (NLP). Data, in this context, are the digital artifacts used to generate AI models and/or employed in combination with AI models during inference. An AI model is an inference method that can be used to perform a “task,” such as prediction, diagnosis, or classification. The model is developed using training data or other knowledge. An AI task is the inference activity performed by an artificially intelligent system.

Tools for training AI models on data are readily available and widely used. What is lacking, however, is a theoretical framework for understanding relationships between data and models. For example, given a specific data set and problem, we lack rigorous methods for identifying the best model, hyper-parameters, and training method to use. Given a specific data set and problem, which additional data would be helpful to include in the training set? What information about a dataset can be deduced from a model trained on the data? What attributes of the data can be reverse engineered from a model? What can we learn about model robustness and transfer learning by looking at relationships between data and models?

The primary focus of this FOA topic is to advance our understanding of the relationship between data and models by exploring relationships among them through the development of FAIR frameworks for relating data and models. Such frameworks should provide capabilities that advance our understanding of AI, provide new insights to help researchers with applications of AI techniques, and provide an environment where novel approaches to AI can be explored.

Proposed frameworks may focus on specific disciplines or sub-disciplines currently supported by SC’s programs in ASCR, Biological and Environmental Research (BER), Basic Energy Sciences (BES), Fusion Energy Sciences (FES), High Energy Physics (HEP), Nuclear Physics (NP), or may focus on particular aspects or sub-areas within AI.