We seek a top scholar at the interface between environmental economics and data science. This scholar will be expected to bring new tools, techniques, and insights to academic, yet policy relevant questions around incentives, markets, and interventions to improve environmental outcomes. The scholar will have expertise in at least one specific environmental challenge such as water, energy, biodiversity, agriculture, climate, or fisheries, and will combine modern data science to address issues around the design or causal evaluation of interventions to address large-scale environmental challenges. The scholarship is expected to be interdisciplinary (for example, the scholar may work with computer scientists, trade economists, or global change ecologists), novel, and policy relevant.
The scholar would contribute to teaching in Bren School’s Master’s in Environmental Science & Management (e.g., in the core class on environmental economics or with a new class using big data for causal policy design) and Master’s in Environmental Data Science (e.g., in a class on causal inference). We will prioritize candidates with both a promising research agenda in environmental economics and leadership skills that can lend support to the Master’s in Environmental Data Science program. Data science is a rapidly growing subfield within environmental economics and is a natural fit for the Bren School, which also has a world-class environmental economics faculty.
The University is especially interested in candidates who can contribute to the diversity and excellence of the academic community through research, teaching, and service as appropriate to the position.
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