*Applications are reviewed on a rolling-basis.

ARS Office/Lab and Location: A research opportunity is currently available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), located in the Western Regional Research Center (WRRC), Albany, California.

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.

Research Project: The selected participant will conduct research applying artificial intelligence (AI) and machine learning (ML) techniques to analyze cervid movement patterns. GPS telemetry data obtained from free ranging cervids will be used by the participant to develop and train custom autoencoders (AE).  These models will be used to identify movement patterns characteristic of chronic wasting disease (CWD) infected wild cervids.

Learning Objectives: The selected participant will be afforded the opportunity to expand their computer and data science skills.  Through collaboration, the participant will have the opportunity to understand cervid behavior and animal movement modelling techniques. The research environment of the WRRC in Albany, California, will provide the participant with opportunities to learn theoretical aspects of chemistry, biochemistry, and mass spectrometry. 

Mentor(s): The mentor for this opportunity is Christopher Silva (christopher.silva@usda.gov). If you have questions about the nature of the research, please contact the mentor(s).

Anticipated Appointment Start Date: 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: The appointment is full time.

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

Citizenship Requirements: This opportunity is available to U.S. citizens only.

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 ORISE.ARS.PacificWest@orau.org and include the reference code for this opportunity.

Qualifications

The qualified candidate should have received a master's degree in one of the relevant fields. Degree must have been received within the past four years by the start of the appointment.

Preferred skills:

• Experience developing artificial intelligence (AI) or machine learning (ML) models, particularly for time series or spatiotemporal data.
• Experience with representation learning, anomaly detection, or unsupervised learning methods.
• Proficiency in Python and familiarity with scientific computing libraries such as PyTorch, TensorFlow, Pandas, NumPy, and related ML frameworks.
• Experience with large datasets, including data preprocessing, feature engineering, and preparation of datasets for robust model training and validation.
• Experience with computational and statistical analysis in Python and R.
• Familiarity with deep learning models such as autoencoders and neural networks.
• Experience with ecological, geospatial, or movement data (e.g., GPS telemetry).
• Strong oral and written communication skills, including the ability to clearly present research findings.
• Ability to collaborate effectively with researchers from multiple disciplines.