Post-Doctoral Fellow – Crop Disease Geo-Spatial / Data Scientist
The International Maize and Wheat Improvement Center, known by its Spanish acronym, CIMMYT®, is a not-for-profit research and training organization with partners in over 100 countries. Please refer to our website for more information: www.cimmyt.org
CIMMYT seeks an innovative, self-motivated, and results-oriented candidate for the position of Crop Disease Geo-Spatial / Data Scientist (Post-Doctoral Fellow) to work on improving maize and wheat agri-food systems in sub-Saharan Africa and other parts of the developing world. The holder of this position should conduct and contribute to collaborative research aimed at improving the monitoring, forecasting, early warning and decision-making around transboundary pests and diseases on wheat and maize, using geo-spatial analysis and big data approaches. The specific research focus of this position is to improve the effectiveness of established crop disease monitoring systems (currently focused on wheat rusts, but includes maize lethal necrosis) through new, innovative geo-spatial analysis and modeling approaches. The position will contribute to several research activities focused primarily on sub-Saharan Africa, but also includes activities in South Asia. The work will involve strong integration and collaboration with internationally renowned crop disease modeling groups.
This post-doctoral fellow will work as a member of CIMMYT’s Socio-Economics Program in close collaboration with other CIMMYT programs and with public, private, local and international partners.
The position will be based at CIMMYT’s Ethiopia office, located in Addis Ababa.
- A recent Ph.D. in geo-spatial science, data science or a quantitatively-oriented science.
- Experience with modeling climatic and/or environmental systems. Crop/animal (human) disease modeling highly desirable.
- Experience with spatial modeling, use of geospatial data, remote sensing and geographical information systems.
- Competency in programming e.g., R and/or Python.
- Strong understanding of database architecture, statistics, data visualization, etc.
- Expertise in working with large data sets and the ability to integrate and interpret multi-thematic data.
- Experience and/or knowledge of crop diseases and epidemiology highly desirable.