Key Dates

None

Office of Nutrition Research (ONR)

National Heart, Lung, and Blood Institute (NHLBI)

National Institute on Aging (NIA)

Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

National Institute of Dental and Craniofacial Research (NIDCR)

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

National Institute of Neurological Disorders and Stroke (NINDS)

National Center for Complementary and Integrative Health (NCCIH)

National Cancer Institute (NCI)

The National Institutes of Health (NIH) Office of Nutrition Research (ONR) and participating NIH Institutes and Centers (ICs) intend to publish a Funding Opportunity Announcement (FOA) for new applications that will support new institutional research training programs (predoctoral, postdoctoral or both) in artificial intelligence (AI) for precision nutrition (AIPrN) that will focus on integration of the domains of precision nutrition, AI including machine learning (ML), systems biology, systems science, “Big Data”, and computational analytics. The goal is to build a future workforce that will be able to use growing data resources to tackle complex biomedical challenges in nutrition science that are beyond human intuition. It is hoped such research will lead to the development of innovative solutions to combat diet-related chronic diseases within the mission areas of the participating ICs.

The vision of the AIPrN training program is to support the development of a diverse research workforce capable who will possess advanced competencies in AI including machine learning and data science analytics to apply to an increasingly complex landscape of “Big Data” from the molecular, to organismal, to community and societal scales related to nutrition and diet related conditions. It emphasizes applications from or collaborations with faculty and/or trainees from minority serving institutions along with other proven approaches to build a diverse workforce. This initiative will compliment and dovetail with the NIH Office of Data Science Strategy's efforts to advance and coordinate Artificial Intelligence research across NIH (https://datascience.nih.gov/artificial-intelligence).

The FOA will utilize the T32 Institutional National Research Service Award (NRSA) activity code and is expected to be published in Summer 2022 with an application due date in Fall 2022.

This Notice is being provided to allow potential applicants sufficient time to develop responsive proposals and consider the requirements that are integral to this initiative.

This Notice encourages investigators with expertise and insights into the area of Artificial Intelligence for Precision Nutrition Science Research (AIPrN) to begin to consider applying for this new FOA. In addition, collaborative investigations combining expertise in Precision Nutrition, AI including machine learning (ML), systems biology, systems science, “Big Data”, and computational analytics will be encouraged, and these investigators should also begin considering applying for this application. Among the areas of research encouraged in this initiative are nutritional sciences, AI including machine learning and data science analytics research examining the mechanisms that underlie diet-related chronic diseases, as well as research designed to improve the translation of existing knowledge of strategies for the prevention and treatment of diet-related chronic diseases.

The details of this institutional T32 training program can be found in the Council of Councils concept clearance brief found at this URL (https://dpcpsi.nih.gov/sites/default/files/Materials-for-Council-AIPrN-8-27-2021.pdf). Briefly, applicants must propose novel interdisciplinary training programs designed for either pre or postdoctoral training, or both. The overarching theme of these programs should address a strategic objective or objectives within the mission of one of the participating NIH institutes. The trainees’ academic backgrounds (computer science or biomedical science) should guide a personalized training plan so that by the end of the program there will be (or has been coming into the program) foundational coursework in diet-related chronic diseases, systems science, and nutrition science along with advanced computational methods. Training should also emphasize principles and practices that promote reproducibility of results and scientific rigor.

Predoctoral trainees for AIPrN should be enrolled in either Ph.D. or equivalent research doctoral degree programs in the biomedical or bioinformatic sciences related to diet-related chronic diseases or nutritional sciences. Alternatively, they may be enrolled in programs anchored in computational science, mathematics, or computer engineering departments. Applications for the AIPrN program include several features that applicants should prepare for:

• Commitment to enhancing workforce diversity. Within the framework of the NIH’s longstanding commitment to excellence, attention must be given to recruitment of trainees from diverse backgrounds, including those from groups nationally underrepresented in biomedical, behavioral and clinical research, individuals with disabilities, and individuals from disadvantaged backgrounds (See Notice of NIH’s Interest in Diversity). The applicants to this training program should elaborate their institutional success at recruiting trainees from diverse backgrounds and to foster their successful completion of the graduate program and transition to their next position. Programs are also expected to expose students to a variety of different topics from discipline-specific faculty.. The applicant training program should either be or consider linking their proposed training programs to trainees and/or faculty of Historically Black Colleges and Universities (HBCUs) or other Minority Serving Institutions (MSIs). Applicants should also consider trainees who have completed an RD-MS or equivalent dietician program as described in the NIH Strategic Plan for Nutrition Research along with other relevant academic backgrounds for this kind of interdisciplinary program. Programs should have flexibility to train those with AI/ML or computational science expertise in the biomedical (diet related disease) sciences or nutrition research or the converse.
• Diversity of Research Topics - While projects selected for training spanning the translational spectrum of the sponsoring institutes are welcome, ideally a number of the potential topic areas proposed should aim to make discoveries from large datasets in order to reduce the rate of diet-related chronic diseases that disproportionally affect minorities or otherwise aim to reduce nutrition health disparities due to race, ethnicity, geographical location, income or educational attainment, and/or address strategies to reduce food insecurity and hunger.
• Plan to Ensure Success of Training Program and Trainees – The application must include a signed letter on institutional letterhead from a President, Provost, Dean, or key institutional leader that addresses 12 specific points describing the activities and resources provided by the institution that will ensure the success of the planned training program and its trainees (not to exceed 10 pages).
• Cross-program team building coordinated by ONR. The Office of Nutrition Research will convene and facilitate annual cross-site meetings among faculty and trainees. The T32 training programs funded through this funding opportunity announcement will be required to participate in this activity that may include periodic training webinars and annual in-person cross-site ONR AIPrN T32 Program grantee meetings. We hope these will allow for exchange of best training practices and courses as well as begin to develop a cadre of connected trainees in this emerging field.

TBD

Applications are not being solicited at this time. 

Please direct all inquiries to:

Christopher Lynch, PhD

NIH Office of Nutrition Research (ONR)

301-325-4232

chrisopher.lynch@nih.gov