Postdoctoral Research in Data Science for Spatial Ecology

~ Andrew Carnegie founded the Carnegie Institution of Washington in 1902 as an organization for scientific discovery. His intention was for the institution to be home to exceptional individuals—men and women with imagination and extraordinary dedication capable of working at the cutting edge of their fields. Today, Carnegie scientists work in six scientific departments on the West and East Coasts. ~

Our legal name, the Carnegie Institution of Washington, has led to confusion because four of our departments are outside Washington and because our legal name does not distinguish us from other non-profits created by our donor. As a result, the institution adopted a new look and name in 2007—the Carnegie Institution for Science. The new name closely associates the words “Carnegie” and “science” and thereby reveals our core identity. The institution remains officially and legally the Carnegie Institution of Washington, but now has a public identity that more clearly describes our work. The institution is additionally confused with other, unaffiliated Carnegies listed at this lnk.

Position Overview

A postdoctoral research position is available for a scientist interested in the application of quantitative data science approaches to regional and global ecological applications.

The position could either be based at the Carnegie Institution’s Department of Global Ecology in Stanford, California, or in the Carnegie Airborne Observatory’s laboratory in Hilo, Hawaii, depending upon the applicant’s interests. The successful applicant will work closely with Carnegie Staff Scientist Greg Asner.

Responsibilities

• The postdoctoral researcher will apply theoretically and methodologically rigorous data science approaches in studies of spatial ecology in terrestrial and marine environments.
• Specific projects will use advanced data science techniques to integrate information from satellite, aircraft (Carnegie Airborne Observatory), and models to study forest and coral reef ecosystems.

Qualifications

• A Ph.D. in a computational data science or closely related field is required.
• A mastery of one or more analytical approaches including deep learning and machine learning, Bayesian statistics, and geospatial modeling is essential to this position.
• Strong computer programming, scientific writing, and English communication skills are required.