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:CVCover LetterList of publications and grantsList 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.