Assistant Professor in Environmental Data Science

The Bren School of Environmental Science & Management (www.bren.ucsb.edu) invites applications for a tenure-track faculty position in Environmental Data Science at the rank of Assistant Professor, to start July 1, 2021. We seek a highly creative and interactive scholar whose research and teaching interests are focused on innovative approaches for computational analyses in an allied discipline of Environmental Science (including, but not limited to, Earth science, ecology, political science, socio-ecological systems, etc.). We particularly seek candidates who address environmental questions using large datasets (e.g., remotely sensed data, text-based repositories, etc.), have a rigorous foundation in statistical fundamentals (e.g., machine learning, Bayesian inference, causal inference, etc.), or leverage cutting-edge developments in data science (e.g., data-driven discovery, scalable computing, etc.) in their work. We welcome candidates whose substantive area of expertise is related to environmental justice, or whose methodological expertise addresses algorithmic bias in quantitative sciences

The Bren School is a graduate school within UCSB that provides rigorous, multi-disciplinary training in environmental science and management to Master's and PhD students. The faculty is drawn from the natural sciences, social sciences, and management.

As a successful candidate, you will develop an internationally-recognized and extramurally-funded research program, mentor graduate students in the candidate’s area of expertise, and teach graduate courses in the School's new Master of Environmental Data Science (MEDS) degree program. All committed, innovative environmental scientists who possess the ability to lead, collaborate, and organize curricular and programmatic needs are encouraged to apply.

UCSB’s academic calendar is based on three 10-week quarters and a 10-week summer session. The expected teaching load for this position is 4 courses per year. In addition, the position will participate in the management and governance of the overall MEDS program.

Basic qualifications for applicants include the completion of all requirements for a PhD except the dissertation in a related field at the time of application. Additional qualifications include applicants must have a PhD at time of hire in a related field and evidence of effective teaching at the university level.

Preferred qualifications also include:

-Candidates whose research and teaching interests are focused on innovative approaches for computational analyses in an allied discipline of Environmental Science (including, but not limited to, Earth science, ecology, political science, socio-ecological systems, etc.).

-Candidates who address environmental questions using large datasets (e.g., remotely sensed data, text-based repositories, etc.), have a rigorous foundation in statistical fundamentals (e.g., machine learning, Bayesian inference, causal inference, etc.), or leverage cutting-edge developments in data science (e.g., data-driven discovery, scalable computing, etc.) in their work.

-Candidates whose substantive area of expertise is related to environmental justice, or whose methodological expertise addresses algorithmic bias in quantitative sciences.