The Alliance of Bioversity International (www.bioversityinternational.org) and the International Center for Tropical Agriculture (CIAT) (www.ciat.cgiar.org) delivers research-based solutions that harness agricultural biodiversity and sustainably transform food systems to improve people’s lives. Alliance solutions address the global crises of malnutrition, climate change, biodiversity loss, and environmental degradation.With novel partnerships, the Alliance generates evidence and mainstreams innovations to transform food systems and landscapes so that they sustain the planet, drive prosperity, and nourish people in a climate crisis.

The Alliance is part of CGIAR, a global research partnership for a food-secure future.

The Alliance is currently searching for a motivated and high performing Postdoctoral Researcher, specializing on climate and environmental data science, to support development of innovative and high-quality analytical solutions to help understand climate impacts, crop and location-specific risks and adaptation options, and inform relevant investments in sustainable agriculture and climate adaptation. The incumbent will contribute to developing analytical and modelling frameworks that integrate biophysical, ecological, and socio-economic domains to evaluate the impacts of various adaptation interventions, considering future drivers of change, such as climate change and economic growth. The Postdoctoral Researcher will design and implement model-based analyses will follow robust conceptual/theoretical frameworks based on recent literature and the IPCC Sixth Assessment Report and will be fully automated using software code (in R and/or Python), documented adequately to ensure replicability. The incumbent therefore will invest time in designing the analytical pipelines and identifying ways to store software code (through version control systems, R packages) and to document these analytical routines for use by others.

Key Responsibilities

In collaboration with partners of the One CGIAR Initiative on “Informing Sustainable Development pathway with Foresight and Data” design of climate data cube structure and data models to support integration of climate variability data. Leverage expertise from the Alliance and CGIAR on data architecture to design and automate ETL (Extract, Transform, Load) processes for the climate data cube that facilitates modeling.
Perform, automate and document analysis of climate data trends and population of climate data cube(s).
Development of geospatial data and models and resultant datasets to support incorporation of key themes (e.g., Biodiversity, farming systems) into other foresight analyses.

Work closely with project partners and other teams in the Alliance to design and perform data analysis for agroclimatic advisory design and deployment, climate risk assessment, through the operationalization of innovative methodologies that take consideration of partners’ and local stakeholder needs.

Contribution to quality assurance and quality control of climate and other related datasets used throughout the project cycle.
Produce scientific papers for peer-reviewed journals and proceedings.

Requirements

• PhD Degree in Climate modeling, agricultural science, data science, environmental science, geography, or any related field with an understanding of climate related risks, climate impacts, and adaptation in agriculture.
• Extensive familiarity and experience working with climate model outputs (e.g., CMIP5/6), historical climate data (e.g., station data, CHIRPS), various remote sensing data and related datasets such as crop yield data, soils data, etc.
• Deep analytics experience using common open-source tools such as R, Python and corresponding spatial libraries (e.g. gdal/ogr, terra, sf, rasterio), and cloud compute platforms such as Google Earth Engine, Microsoft Planetary Computer.
• Understanding of CMIP6 climate data structures such as NetCDF and Zarr, as well as familiarity with a variety of climate science related libraries including those used in cloud computing of climate data.
• Experience summarizing climate data trends and climate variability at various spatiotemporal scales using common indices and metrics.
• Expertise in modeling approaches that uses climate and other environmental data as input, such as maxent, random forest, Ecocrop, and in integrating other model results (e.g., crop models) with spatial datasets.
• Familiarity with RS-derived land use and biodiversity datasets, and landscape metrics.
• Strong teamwork, interpersonal and communications skills; courtesy, tact, and the ability to establish and maintain effective working relationships with people of different cultural and national backgrounds.
• Excellent command of English language, both written and spoken.
• Strong analytical and synthesizing skills; ability to think and write clearly and concisely on
• Past experience in international agricultural research.
• Experience with interdisciplinary research.
• Peer-reviewed publications.
• Experience using cloud computing infrastructure for computing and storage a plus.
• Experience with Google Earth Engine a plus.
• Highly developed skills with personal organization and priority setting.
• Ability to work with a high degree of independence within assigned areas complemented by sound judgment and initiative, and the ability to maintain structured and ad hoc communications with supervisors and colleagues to seek guidance.
• Willingness and enthusiasm to learn and practice new technologies as needed.