Research Specialist – Computational Agronomist
JOB DETAIL
About the position
The Climate Action research area is seeking a computational agronomist with experience in various modeling techniques to enhance decision-making in the agricultural sector. Specifically, expertise in agroclimatic modeling. The selected candidate will be responsible for designing and implementing Decision Support Tools (DST) aimed at improving agronomic management and promoting climate adaptation in small-scale productive systems in Latin America, initially focusing on Honduras. These DSTs will provide agronomic advice, to implement AgWise, a site- and climate-specific agronomic recommendation engine. This effort will build capacity within other teams of the Alliance Bioversity-CIAT in Latin America, particularly in Multifunctional Landscapes, as these functions will contribute to the Honduras Soils Project, which revolves around the continuous development, maintenance, and scaling of decision support systems supporting the generation and delivery of recommendations to improve fertilizer use.
For this position, a deep understanding of crop modeling is necessary, including to some extent machine learning and artificial intelligence models, as well as process-based crop models, encompassing their development, calibration, and specialization for agronomic applications. Additionally, knowledge of agronomy, fertilization, and crop nutrition, as well as plant physiology and the soil-plant-atmosphere system interaction, is required, as the candidate will need to adapt and interpret crop modeling outputs to inform decisions that optimize yields through fertilization strategies.
The position will be based in the operations center of the Americas, located in the Campus of Palmira, Colombia.
Responsibilities
- Design, calibrate, and implement agronomic crop models based on process-based and empirical models, such as statistical and machine learning models, to support the development of various Decision Support Tools (DST).
- Contribute to the development of robust and practical conceptual and modeling frameworks for the implementation of agronomic advice in Latin America, with a special emphasis on Honduras.
- Lead specific activities that help modularize the “white label” framework for DST (AgWISE) within the Honduras Soils Project, building on existing efforts and learnings.
- Implement practical routines to reduce errors and uncertainties through the incorporation of remote sensor image analytics into AgWISE workflows.
- Collaborate effectively to build capacities among specific members within the Alliance in using models to develop agronomic recommendations.
- Work closely with soil experts, coordination staff, and software engineers to support the implementation of DST in crop fertilization recommendations in Honduras.
- Use various geospatial data sources, such as soil, crop distribution, and climate, along with agronomic management records and crop or variety performance, to enhance and localize crop recommendation systems.
- Lead and contribute to technical discussions with partners to develop roadmaps for DST implementation.
- Contribute and lead the writing of reports and scientific articles related to DST for agronomic recommendations.
- Participate in drafting internal project proposals as deemed necessary and relevant.
Requirements
- Bachelor’s degree in agronomy, data science, geography, or related fields.
- Advanced studies (master’s or PhD degree), in data science, computational agronomy, geographic information systems, agronomic and/or agroclimatic modeling, computer vision, artificial intelligence, or similar disciplines.
- At least 5 to 7 years of experience in data science or similar roles to the vacancy.
- Extensive experience, training, and knowledge in crop modeling
