The Quadracci Sustainable Engineering Lab (QSEL) works on solutions that could advance well-being and economic growth through both access to energy, water and infrastructure as well as creating and enabling income generating opportunities in emerging economies. In recent years, we have been increasingly asked to help identify opportunities and binding constraints at granular scale using national-scale data of populations, land-use, economic activity and infrastructure to support the ability of private sector and governments to leverage opportunity and address constraints. The incumbent will be contributing to ‘Using Data to Catalyze Energy Investments’, a Columbia World Project.
To utilize a combination of field data, utility data, existing survey data, imagery from satellites and ground-based cameras to characterize and classify agricultural lands, especially for presence and/or proximity of surface and shallow groundwater and the presence and potential for irrigation. The postdoctoral researcher will work on sampling frameworks to gather field data, and assimilate a variety of other legacy data to produce representative land-scape classifications, as well as generate labels from imagery. Preference will be given to candidates with experience in spatial sampling methods designed to combine higher cost interviews, view lower cost visual observations from the field and high-resolution satellite imagery. Another key responsibility will be summarizing and verifying data received by field surveying teams using R and Python. Experience with developing automated scripts to manipulate and analyze field collected data and multispectral remotely sensed data sets are also plusses.
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