Postdoctoral Research Associate

Job Title

Postdoctoral Research Associate

Agency

Texas A&M Agrilife Research

Department

Soil & Crop Sciences

Proposed Minimum Salary

Commensurate

Job Location

College Station, Texas

Job Type

Staff

Job Description

Job Responsibilities:

-Design and implement AI/ML models to analyze large-scale agricultural datasets (e.g., field trials, satellite imagery, IoT sensor data).

-Develop pipelines for preprocessing, integration, and modeling of heterogeneous data (spatial, temporal, tabular) -Conduct research in explainable AI and uncertainty quantification applied to agronomic decisions.

-Collaborate with agronomists, soil scientists, engineers, and other domain experts.

-Lead manuscript writing and present findings at conferences.

-Initiate and support grant writing and development of externally funded research proposals.

-Other duties as required.

Required Education:

-Ph.D. in Soil and Crop Sciences, Statistics, Data Science, Computer Science, Agricultural Engineering, or a closely related field.

Required Knowledge, Abilities and Skills:

-Strong analytical, organizational, computer and communication skills.

-Ability to multi task and work cooperatively with others.

Preferred Knowledge, Abilities and Skills:

-Strong background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow.

-Experience working with real-world datasets, especially those that are noisy, sparse, or high-dimensional.

-Demonstrated record of peer-reviewed publications

-Experience with agricultural or environmental datasets (e.g., UAV, hyperspectral, soil health, crop yield).

-Familiarity with geospatial data and tools (e.g., GIS, QGIS, Google Earth Engine).

-Knowledge of explainable AI (e.g., SHAP, LIME), model interpretation, and/or uncertainty quantification.

-Familiarity with reproducible workflows and tools such as Git, Docker, or Jupyter Notebooks.

-Interest in mentoring students and contributing to a collaborative research culture Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow.

-Experience working with real-world datasets, especially those that are noisy, sparse, or high-dimensional.

Please attach to your completed application:

CV

Cover Letter

List of publications and grants

List of references (3) with email and daytime phone number(s)

All positions are security-sensitive. Applicants are subject to a criminal history investigation, and employment is contingent upon the institution’s verification of credentials and/or other information required by the institution’s procedures, including the completion of the criminal history check.

Equal Opportunity/Veterans/Disability Employer.