Crop Science and Remote Sensing Specialist-CIMMYT

This role focuses on three core areas: agronomy, crop physiology, and remote sensing/geospatial analytics. The specialist will design, implement, and oversee on-station and on-farm agronomic trials aligned with breeding and seed systems research objectives. Responsibilities include optimizing agronomic packages such as plant population, nutrient management, water management, and cropping systems to ensure standardized protocols and data quality across multiple locations. The role involves monitoring crop growth and field conditions, maintaining accurate agronomic records, and analyzing trial data to contribute to the interpretation of genotype-by-environment-by-management (G×E×M) interactions. Additionally, the specialist will identify and quantify key physiological traits associated with yield, stress tolerance, and resource-use efficiency, supporting phenotyping for drought, heat, nutrient stress, and other priority constraints through standardized protocols. Deployment and operation of remote sensing tools such as UAVs/drones, handheld sensors, and proximal platforms for data collection, processing, and analysis of imagery and sensor data are also required. The specialist will integrate remote sensing and GIS data with field and physiological data for high-throughput phenotyping, produce spatial maps and dashboards for technical reporting, and map potential distribution areas of various stresses, diseases, and pests using environmental variables. Capacity building and synthesis of findings for reporting are integral components of the role.

• Master’s degree (PhD preferred) in Crop Science, Remote Sensing, GIS, Agricultural Engineering, Data Science, or a related field. • Minimum of 5 years of relevant professional experience in crop science, remote sensing, or digital agriculture. • Strong proficiency in remote sensing tools (e.g., ENVI, ERDAS Imagine, SNAP) and GIS software (e.g., ArcGIS, QGIS). • Experience with satellite imagery analysis, drone data processing, and machine learning for agricultural applications. • Proficiency in programming languages such as Python, R, or MATLAB for data analysis and modeling. • Experience working with agricultural datasets, including crop yield data, weather data, and soil data. • Strong analytical and problem-solving skills with the ability to interpret complex datasets. • Excellent communication skills with the ability to present technical information to non-technical stakeholders. • Experience in project management and collaboration with multidisciplinary teams. • Fluency in English (additional languages such as French or Spanish are an asset). • Willingness to travel and conduct fieldwork in remote locations.