Research Assistant Professor

Research Assistant Professor

This position is funded by a USDA-funded research project. The primary purpose of the position is to monitor the conditions of crop, pasture, and their growth environment to support agricultural decision making with advanced remote sensing and geospatial technologies.

Responsibilities:

• Develops advanced Agro-geoinformatic algorithms for monitoring and predicting the conditions of crop, pasture, and their environment with advanced remote sensing and geospatial technologies;
• Develops and refines algorithms and workflows for crop and pasture monitoring, modeling, prediction, and decision support and automation;
• Supervises graduate research assistants and student interns working on the project; and
• Publishes and disseminates research at top-tier journals and conferences.

Required Qualifications:

• Terminal degree in a related field;
• One-year post-Ph.D. research experience in related field at the time of appointment;
• Research experience in agricultural remote sensing, crop and pasture modeling, or agricultural environment monitoring and prediction;
• Knowledge of remote sensing, geospatial information systems, crop modeling and prediction, Agro-AI/ML, or agricultural digital twin; and
• Knowledge of relevant programming skills.

Preferred Qualifications:

• At least two-years post-Ph.D. research experience in related fields;
• Track record that demonstrates the ability to work well with an interdisciplinary research team;
• Excellent publication records;
• Experience in large-scale earth observation analytics;
• Experience in integrating multi-source remote sensing data with process-based or data-driven models to characterize agricultural and ecological systems;
• Experience in applying advanced Artificial Intelligence/Machine Learning (AI/ML) methods in agriculture (Agro-AI/ML); and
• Experience in programming with multiple languages (e.g., Java, C/C++, Python) for geospatial information systems, agro-informatic applications, agricultural monitoring and modeling, Agro-AI/ML, or digital twin.

Instructions to Applicants:

For full consideration, applicants must apply for the Research Assistant Professor at Complete and submit the online application to include three professional references with contact information, and provide a cover letter and CV for review.