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A complete application consists of:

• An application
• Transcript(s) – For this opportunity, an unofficial transcript or copy of the student academic records printed by the applicant or by academic advisors from internal institution systems may be submitted.
• A current resume/CV, including academic history, employment history, relevant experiences, and publication list.
• Two educational or professional recommendations

All documents must be in English or include an official English translation.

• Applications are reviewed on a rolling basis.

ARS Office/Lab and Location: A postdoctoral research opportunity is available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), Stored Product Insects and Engineering Research Unit in Manhattan, KS, and participating collaboratively with the Crop Improvement and Genetics Research Unit in Albany, CA, and the Food Processing and Sensory Quality Research Unit in New Orleans, LA.

The U.S. Department of Agriculture - Agricultural Research Service (USDA ARS) mission involves problem-solving research in the widely diverse food and agricultural areas encompassing plant production and protection; animal production and protection; natural resources and sustainable agricultural systems; and nutrition; food safety; and quality. The programs are conducted in 46 of the 50 States, Puerto Rico, and the U.S. Virgin Islands. For ARS to maintain its standing as a premier scientific organization, major investments in computing, networking, and storage infrastructure are required. Training in data and information management are integral to the integrity, security, and accessibility of research findings, results, and outcomes within the ARS research enterprise. Nearly 2000 scientists and postdoctoral fellows conduct research within the ARS research enterprise.

Research Project: The SCINet/Big Data Research Participation Program of the USDA ARS offers research opportunities to motivated postdoctoral fellows interested in solving agriculture-related problems at a range of spatial and temporal scales, from the genome to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning, to help solve complex agricultural problems that also depend on collaboration across scientific disciplines and geographic locations. In addition, many of these technologies rely on the synthesis, integration, and analysis of large, diverse datasets that benefit from high performance computing (HPC) clusters. The objective of this fellowship is to facilitate cross-disciplinary, cross-location research through collaborative research on problems of interest to each applicant and amenable to or requiring the HPC environment. Training will be provided in data science, scientific computing, AI/machine learning, and related topics as needed for the fellow to complete their research.

This research project will define the structure of insect enzymes, cereal proteins, and nut allergens to improve agricultural products and develop potential therapeutics and control products for storage pests. Gluten intolerance is a major health problem for some individuals, and stored product insects have adapted enzymes to digest a major gluten protein, prolamin, and also may have the ability to hydrolyze nut allergens. One group of digestive enzymes in Tribolium castaneum, cathespins, have potential as therapeutics and also as novel targets for insect control products. The fellow will combine extensive biochemical and molecular data on T. casteneum cathepsins with structural analyses to identify prolamin- and nut allergen-specific hydrolyzing clades. AlphaFold2 models of insect cathepsins will be compared to the structure of other glutenases, e.g., bacteria and fungi, for conserved folds or motifs to build a database of insect enzyme structures and substrate protein molecules. Some preliminary protein structure predictions have been shared through the AlphaFold2 collaboration and builds using I-TASSER, Quanta, and CHARMM. Specialized programs will be used to understand the folding of proteins in a specialized environment such as the pH-compartmentalized insect gut.

Learning Objectives: The fellow will co-lead collaborative research on insect functional genomics, cereal science, and nut allergens, providing crucial data on protein structure (with interactions of insect enzymes and prolamins in cereals or allergens in nuts) for a systems approach to understand how insect enzymes can be used to hydrolyze prolamins and allergens that are problematic for individuals with gluten intolerance and severe allergies, respectively. Training will include specialized software for protein research to familiarize the fellow with tools/training available through SCINet CERES and ATLAS. Although the protein folding predictions of AlphaFold2 are groundbreaking, the fellow will seek to develop foundations to solve research-motivated problems in agriculture and establish workflows for protein-protein interactions in other research areas. The fellow will have the opportunity to apply tools and methods developed during this project to uncover new enzyme-substrate interactions that will have major impacts on health, pest control, plant breeding, and food security. Currently, it is important to build multidisciplinary expertise, and the interactions between the three research units interfacing with high performance computing environments will help to set trends in research.

Mentor(s): The mentor(s) for this opportunity is Brenda Oppert ([email protected]). Please contact the mentor if you have questions about this opportunity.

Anticipated Appointment Start Date: 2023; start date is flexible and will depend on a variety of factors.

Appointment Length: The appointment will initially be for two years but may be renewed upon recommendation of ARS and is contingent on the availability of funds.

Level of Participation: The appointment is full-time.

Participant Stipend: The participant will receive a monthly stipend commensurate with educational level and experience. The current stipend range for this opportunity is $85,000 - $95,000/year plus a supplement to offset a health insurance premium.

Citizenship Requirements: This opportunity is available to U.S. citizens, Lawful Permanent Residents (LPR), and foreign nationals. Non-U.S. citizen applicants should refer to the Guidelines for Non-U.S. Citizens Details page of the program website for information about the valid immigration statuses that are acceptable for program participation.

ORISE Information: This program, administered by ORAU through its contract with the U.S. Department of Energy (DOE) to manage the Oak Ridge Institute for Science and Education (ORISE), was established through an interagency agreement between DOE and ARS. Participants do not become employees of USDA, ARS, DOE or the program administrator, and there are no employment-related benefits. Proof of health insurance is required for participation in this program. Health insurance can be obtained through ORISE.

Questions: Please visit our Program Website. After reading, if you have additional questions about the application process, please email [email protected] and include the reference code for this opportunity.

The qualified candidate should have received a doctoral degree in one of the relevant fields or be currently pursuing the degree to be received before December 31, 2023.

Preferred Skills:

• Experiences in molecular biology
• Experience developing, testing, and refining machine learning models
• Experience in protein chemistry
• Excellent written and oral communication skills.
• Experience in team and collaborative scientific environments.

• Degree: Doctoral Degree.
• Discipline(s):
  • Computer, Information, and Data Sciences
  • Engineering
  • Life Health and Medical Sciences
  • Other Physical Sciences