Data Science Approaches to Ecological Forecasting
Carl Boettiger, Professor
Environmental Science, Policy and Management
Applications for Fall 2024 are closed for this project.
Develop, test, and visualize methods for forecasting ecological variables such as carbon flux, beetle abundance, or indicators of aquatic ecosystem health.
Role: Students will implement existing and cutting edge forecasting techniques using both process-based statistical models and machine learning techniques to forecast a variety of ecological processes monitored by the National Ecological Observatory Network. Students will develop automated forecast systems which submit predictions to the Ecological Forecasting Initiative annual challenge.
Qualifications: Candidates should be self-motivated, curious, and able to collaborate effectively and professionally in an online environment. Applicants should have some prior experience with either Python or R, be familiar with the use of git/GitHub, and have an interest in applications to issues in ecology, environment and biodiversity.
Hours: to be negotiated
Related website: https://carlboettiger.info
Related website: https://carlboettiger.info