GeoField Fellow (Summer 2023)

AidData invites applications for two temporary, three-month Graduate Fellowships during the summer of 2023. Through this fellowship, individuals will engage in cutting-edge geospatial research, including research design, data collection and analysis, and manuscript preparation. The objective of this fellowship is to develop and apply critical geospatial skills for academic and non-academic purposes.

Primary responsibilities include:

• Develop and program workflows in cloud computing environment (e.g., Google Earth Engine – GEE) and open-source statistical software (i.e., R) to transform time series remotely sensed imagery and open-source data into outcomes that help to understand socio-ecological, agriculture, and environmental issues in various parts of the world, specifically Asia and Africa.
• Automate geospatial workflows designed for estimating environmental indicators related to climate-sensitive agriculture with thorough testing and documentation.
• Conduct literature reviews in fields related to geospatial data and techniques (e.g., machine learning), particularly environmental and agricultural remote sensing.

This position is based at AidData at William & Mary's Global Research Institute in Williamsburg, Virginia. This position is a temporary (hourly) position not to exceed 6 months. This is a restricted position subject to availability of funding. This position is remote-work eligible (works remotely at least 1 day per week but not full-time). Remote work eligibility is not guaranteed and is subject to approval.

Required Qualifications

• Master’s degree or the first year of a Ph.D. in Physical Geography, Computer Science, Spatial/Geospatial Science, Forestry and Natural Resources, Spatial Hydrology, and Environmental Engineering, or related field.
• Successfully completed coursework in Geospatial Science (i.e., remote sensing and Geographic Information Systems – GIS) and have advanced skills in geospatial analysis with a text-based interface (i.e., R, GEE, Python).
• Prior experience with machine learning techniques
• Experience working with time series spatial data (e.g., remotely sensed imagery, in-situ observations)

Preferred Qualifications

• Programming in R and in GEE via JavaScript
• Previous experience working with interdisciplinary research teams
• Knowledge of eco-hydrologic models