Location: Maricopa, AZ
Cap-Exempt H-1B Position — No lottery required
**Posting Number** req25891 **Department** Biosystems Engineering-Res **Department Website Link** https://be.arizona.edu/extension **Location** Maricopa **Address** 37860 W Smith Enke Rd, Maricopa, AZ 85138 USA **Position Highlights** The postdoctoral position focuses on data-driven analysis and prediction of crop evapotranspiration using Machine Learning and Artificial Intelligence. The role integrates multi-source datasets, including remote sensing, climate variables, and field-collected plant and soil data, to develop predictive ET models. The candidate will apply expertise in Geographic Information Systems (GIS), environmental modeling, and data analytics to assess spatial and temporal ET variability. Knowledge in Environmental Engineering and Hydrogeophysics will support modeling of soil-water-plant interactions under varying environmental conditions. The postdoc will assist with field data collection for model validation and experimental support. Responsibilities include advanced data analysis, model development, and publication of results in peer-reviewed and extension journals. *Outstanding U of A benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; U of A/ASU/NAU tuition reduction for the employee and qualified family members; access to U of A recreation and cultural activities; and more!* The University of Arizona has been recognized for our innovative work-life programs. **Duties & Responsibilities** **Duties and Responsibilities:** - Develop and apply Machine Learning and Artificial Intelligence models to predict crop evapotranspiration (ET) using multi-source environmental datasets. - Integrate and analyze remote sensing, climate variables, and field-collected plant and soil data to support accurate ET modeling. - Utilize GIS and environmental modeling to assess spatial and temporal variability in ET and soil-water-plant interactions. - Support irrigation research through field data collection, data quali