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modeling. Experience in large spatial data processing, analysis, and interpretation. Experience in running and evaluating environmental models. Good publication record. Good speaking and writing
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and established on-farm trials in Africa. Conduct spatial analysis to predict yield at scales. Contribute to the overarching goals of the research project team. Supervise master and doctoral student
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computational power and the increasing availability of large volumes of remote sensing data with finer spatial and temporal resolutions have significantly transformed the way we approach climate and weather
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spatial distribution of critical topsoil properties in global drylands. Process large-scale geospatial and remote sensing datasets using High Performance Computing (HPC) systems. Conduct data analysis, and
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, large-scale measurements that can be used to estimate surface variables of interest. In particular, radar data can nowadays be used to obtain maps of surface soil moisture at a high spatial resolution (a
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review publications. Develop and implement simulation models to predict yields from secondary data sources and established on-farm trials in Africa. Conduct spatial analysis to predict yield at scales
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computer programming tools such as Matlab and/or Python. Knowledge of statistics and mathematical modeling. Experience in large spatial data processing, analysis, and interpretation. Experience in running
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international users in water and food systems. Job Description: Over the past few years, advances in computational power and the increasing availability of large volumes of remote sensing data with finer spatial