**Job Description**
This Postdoctoral Research Associate position involves designing and implementing AI/ML models to analyze large-scale agricultural datasets, including field trials, satellite imagery, and IoT sensor data. Key responsibilities include developing pipelines for preprocessing, integration, and modeling of heterogeneous data, conducting research in explainable AI and uncertainty quantification applied to agronomic decisions, and collaborating with various domain experts. The role also requires leading manuscript writing, presenting findings at conferences, and supporting grant writing.
**Skills & Abilities**
• Strong analytical, organizational, computer and communication skills.
• Ability to multi task and work cooperatively with others.
• 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.
**Qualifications**
Required Degree(s) in:
• Soil and Crop Sciences
• Statistics
• Data Science
• Computer Science
• Agricultural Engineering
• Closely related field
Note: We’ve analyzed the actual job post using AI, for more details visit the original job post by clicking on “Apply Now”!
