Jobs · Hawaii

USDA-ARS Fellowship in Computational Modeling for Invasive Tropical Pest Management

Contract

ARS Office/Lab and Location: A research opportunity is currently available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), located at the Tropical Crop and Commodity Protection Research Unit (TCCPRU) in Hilo, Hawaii.

The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief scientific in-house research agency with a mission to find solutions to agricultural problems that affect Americans every day from field to table. ARS will deliver cutting-edge, scientific tools and innovative solutions for American farmers, producers, industry, and communities to support the nourishment and well-being of all people; sustain our nation’s agroecosystems and natural resources; and ensure the economic competitiveness and excellence of our agriculture. The vision of the agency is to provide global leadership in agricultural discoveries through scientific excellence.

About the role

The mission of the Tropical Crop and Commodity Protection Research Unit is to develop pre and postharvest technologies and management strategies for invasive pests, and to open and maintain market access and improved quality of tropical fruit, vegetable and ornamental crops grown in the Pacific Basin.

During this fellowship you will engage with research to extend existing computer models of surveillance traps for invasive insects, necessary to protect United States agriculture from the threat of foreign pest species. You will also have the opportunity to assist in the day-to-day operations of a laboratory, including design and execution of experiments, data collection, and data summarization.

  • Help implement computer code to simulate insect invasions.
  • Search the literature for parameters to make models realistic.
  • Analyze output from simulation runs using established statistical techniques.

Opportunity exists for novel approaches, and you will be encouraged and supported to publish results of your work in peer-reviewed journals. This opportunity will provide exposure and practice with risk management and modeling of invasive pest insects and the training necessary to prepare the student for an exciting career in agricultural research.

Learning Objectives

  • Learn to extend and refine computational models used to simulate surveillance and management strategies for invasive insects.
  • Develop skills in programming and code implementation to build and run invasion-simulation models.
  • Strengthen abilities in literature review and parameter identification to support realistic model development.
  • Gain experience analyzing model outputs using established statistical techniques to interpret invasion dynamics and risk patterns.
  • Build foundational laboratory research skills, including experimental design, data collection, and data summarization.
  • Understand principles of risk management and biological invasion modeling within the context of agricultural pest threats.

Requirements

  • Candidate should have a Bachelor's or Master's degree in Mathematics, Physics, Engineering, Quantitative Biology, or a related field of study.
  • Knowledge of mathematical modeling, computer programming, statistics, and data collection are desirable.
  • Degree must have been received within the last five years.
  • This opportunity is available to U.S. citizens only.

Appointment Details

  • Anticipated appointment start date: October 5, 2026. Start date is flexible and will depend on a variety of factors.
  • Appointment length: The appointment will initially be for one year, but may be renewed upon recommendation of ARS and is contingent on the availability of funds.
  • Level of participation: Full-time.

Pay

The participant will receive a monthly stipend commensurate with educational level and experience.

Mentor

The mentor for this opportunity is Nicholas Manoukis (Nicholas.Manoukis@usda.gov). If you have questions about the nature of the research, please contact the mentor.

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