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Postdoctoral Research Associate Position in Agricultural Economics, Occupational Health, and Machine Learning at the University of California Davis, Department of Agricultural and Resource Economics and the Western Center for Agricultural Health and Safety (WCAHS) at the University of California, Davis

The ideal candidate for the postdoc will combine a strong understanding of agricultural production and the tasks typically performed, potentially through their expertise in agricultural economics, and an understanding of frontier methods in Natural Language Modeling, Large Language Models, and Big Data Management and Analytics. The candidate will be responsible for compiling and harmonizing data on occupational injuries in agriculture from multiple sources. They will develop and apply machine learning algorithms to extract attributes of the worker, employer, and primary product (type of crop or animal), classify the task being performed at the time of the injury and, where possible, attribute the cause of the injury. They will compute incidence rates for multiple categories of occupational injuries across the features identified through the machine learning exercise. They will maintain and publish these incidence rates as a regularly updated data series, summary dashboard, and bi-annual report on occupational injuries in agriculture in the Western states. The candidate will prepare at least one peer-reviewed publication per year and will also have opportunities to explore independent lines of research inquiry. The candidate will be based in the Department of Agricultural and Resource Economics at UC Davis.

Minimum qualifications:
PhD in agricultural economics, economics, statistics, or computer science.

Preferred qualifications:
Prior experience with machine learning algorithms with an emphasis on Natural Language Modeling and Large Language Models; expertise in economics, with a familiarity with agricultural labor or agricultural production systems preferred; strong communication and organizational skills; and exceptional data management skills and interest in transparent and reproducible science.

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