Research Management Coordinator – Land and Food System Modelling

The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) seeks to recruit Research Management Coordinator – Land and Food System Modelling who will provide support to the Multifunctional Landscapes Lever of the Alliance in Ethiopia and beyond for the spatially explicit analysis of landscape analysis (hydrology, land-use change), socioeconomic integration to support national and regional development pathway development and food systems transformation in Ethiopia.  She/he will also support projects related to landscape research and integration of these far and landscape level analysis to regional and national development pathways using established models like FABLE and other impact and foresight models.  She/he will be a member of the Alliance which is based at the International Livestock Research Institute (ILRI) campus in Addis Ababa.

The candidate will integrate remote sensing, hydrological modelling, and spatial food-systems analysis to generate evidence that supports national food and land use system pathways in Ethiopia and East Africa. She/he will specifically:

• →Design and apply machine learning and spatially explicit deep learning frameworks for land-use scenario trade-off analysis within land management pathways (irrigation expansion, afforestation, catchment restoration);
• →Lead the spatial downscaling of FABLE national food and land-use pathways to regional and sub-national scales in Ethiopia zonal statistics extractor to translate aggregate national scenarios into spatially explicit maps of projected cropland, forest, pasture, and land-cover change by region and agroecological zone;
• →Conduct spatially explicit analysis to identify Ethiopian landscapes where agroecological practices (ISFM, cover crops, agroforestry, diversified farming, no-till) offer the greatest co-benefits for food security, biodiversity, soil organic carbon, and GHG emission reduction, combining FABLE practice coefficient data with landscape-scale biophysical spatial layers;
• →Process and analyze multi-temporal satellite imagery (Landsat, Sentinel) in Google Earth Engine to monitor long-term dynamics of land and food system pathway modelling for Ethiopia's and beyond.
• →Develop spatially explicit decision-support tools and interactive dashboards that communicate land and food system hotspots (land-use projections, water stress hotspots, agroecological suitability maps, GHG emission spatial distributions)  to national policymakers, regional planners, and food system stakeholders;
• →Support FABLE-C Ethiopia scenario parameterization by compiling and harmonizing spatially disaggregated data on crop productivity, livestock distribution, irrigation potential, and deforestation pressure across Ethiopia's administrative regions and major agroecological zones, feeding into FABLE Calculator calibration and different scenario development;
• →Publish scientific findings in peer-reviewed journals on landscape hydrology, remote-sensing-based water monitoring, and spatially explicit food-systems modelling; communicate results to diverse audiences including scientists, policymakers, regional governments, and development partners through technical reports, policy briefs, blogs, and webinars/training sessions;
• →Contribute to resource mobilization by co-authoring research proposals targeting international development partners and supporting capacity building in national partner institutions on GEE-based spatial analysis, FABLE nexus tools, and hydrological modelling methods;
• →Develop and strengthen partnerships with Ethiopia's Ministry of Agriculture, Ministry of Water and Energy, regional environmental and agricultural bureaus, and universities to co-develop monitoring frameworks that link hydrological data, land-use change evidence, and food system pathway analysis for integrated national planning;
• →Ensure rigorous research data management, open-science practices, and FAIR data principles — including maintaining version-controlled public repositories (GitHub) for all code and spatial workflows — to maximize reproducibility and utility of outputs for national partners and the FABLE Consortium.

We thank all applicants for their interest in working for ABC/ILRI. Due to the high volume of applications, only shortlisted candidates will be contacted.

ABC/ILRI does not charge a fee at any stage of the recruitment process (application, interview meeting, processing or training).

To find more about the Alliance, visit our Website at https://alliancebioversityciat.org

• Masters’ degree or PhD in Geography, Environmental Studies, Hydrology, Remote Sensing, Food Systems Modelling, or a related field, with demonstrated expertise in geospatial analysis and quantitative environmental modelling.

• In depth knowledge in a technical and specialized field of study.

• Master’s degree with 9 years or PhD degree with 2 years of professional experience

• Demonstrated expertise in processing local, regional, national biophysical and socio-economic data environmental monitoring applications.
• Proven experience in spatial downscaling of national-level models or projections to sub-national / regional scale;
• Experience compiling, harmonizing, and managing open-access global and national geospatial datasets (land cover, climate, vegetation indices, water bodies) with complete metadata for use in modelling platforms.
• Publication record in peer-reviewed journals spatial food-systems / landscape modelling.

• Strong theoretical and applied knowledge in satellite image processing, spatiotemporal data analysis, and machine learning / deep learning methods for landscape and hydrological applications.
• Familiarity with AFOLU nexus models for agriculture, food and land use dynamics is a strong advantage.
• Proficiency in developing and applying spatial machine learning and deep learning models for environmental land and food system application.
• Proficiency in Python and/or R for geospatial data analysis, statistical modelling, and scientific visualization; experience with spatial network analysis
• Understanding of catchment hydrology, lake ecosystem dynamics, and climate–land surface interactions and their relevance to food and land-use systems.
• Excellent communication and interpersonal skills; ability to engage diverse stakeholders including national government ministries, CGIAR research programs, and international development partners.
• Excellent command of English, both written and spoken

More ILRI jobs

Subscribe by email to ILRI jobs alert