Data Science Approaches to Ecological Forecasting
Carl Boettiger, Professor
Environmental Science, Policy and Management
Closed. This professor is continuing with Fall 2024 apprentices on this project; no new apprentices needed for Spring 2025.
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