Postdoctoral Research Associate

About the role

Texas A&M AgriLife Research at Temple is seeking a highly motivated Postdoctoral Research Associate with expertise in precision agriculture technologies, digital soil mapping/pedometrics, proximal/remote sensing, and geospatial modeling. The ideal candidate will work and collaborate closely within a research team at Texas A&M AgriLife and USDA-ARS laboratories to advance precision conservation in cropping systems with diverse management, delivering practical soil and agronomic information for decision-making at the subfield, farm, and regional levels.

Responsibilities

• Combine field observations with proximal sensing, UAV and satellite imagery, yield monitor data, and soil, environmental, and weather datasets to create soil and agronomic intelligence products that guide precision management at the sub-field level and inform conservation decisions.
• Integrate multi-source datasets, analyze yield stability over time, build and validate ML/AI-based prediction models, and create field zones tied to management actions supported by farm economics.
• Develop scaling-up solutions for different management scenarios, including profit–risk–environment tradeoff products, reusable R/Python workflows, and results to be published and presented in collaboration with USDA-ARS and university partners across Texas and the U.S.
• Lead and co-author peer-reviewed scientific publications and actively contribute to proposal development.
• Perform other duties as assigned.

Requirements

• Ph.D. in Soil Science, Agronomy, Agricultural Engineering, Geosciences, Environmental Sciences, or a closely related discipline.
• Strong background in data-intensive soil and agronomic analytics and geospatial modeling.
• Proficiency in proximal sensing, GIS, and remote sensing.
• Ability to multi-task and work cooperatively with others.

Preferred Qualifications

• Advanced ML/AI experience in digital soil mapping/pedometrics and soil landscape modeling, and in handling high-resolution geospatial and temporal datasets.
• Demonstrated experience with precision agriculture data/tools (yield monitor data, spatial variability, management zones, ECa/EMI, LiDAR, VisNIR, and UAV workflows).
• Proficiency in programming languages (R or Python) for automated reproducible workflows.
• Knowledge, experience, and interest in assessing the impacts of management practices on environmental outcomes, such as soil health diagnostics, water quality, carbon/nitrogen cycling, and profit–risk–environment tradeoff products.
• Excellent academic record, including authored/co-authored publications and contributions to significant scientific meetings, seminars, and conferences.
• Strong oral and written communication skills.

This position is grant funded and availability is contingent on grant funding.