Position description
The Landscapes of Change (LOC) Lab at the Bren School of Environmental Science & Management invites applications for a postdoctoral researcher in forest ecology and data science. The successful candidate will join a dynamic, interdisciplinary group investigating how climate change is reshaping forests through shifting disturbance regimes. The position centers on data-driven research using large, multi-source datasets to understand how drought, fire, bark beetles, disease, and their interactions influence forest structure, function, and recovery.
We are especially interested in candidates with strong quantitative and computational skills, experience in big-data analysis, and a deep interest in forest disturbance dynamics. We welcome applicants who enjoy building reproducible, data-intensive workflows and contributing to a collaborative research culture that spans multiple disciplines and career stages.
Project/Position description:
-Harmonize field-based observations with remotely sensed datasets (e.g., Landsat, MODIS) across spatial and temporal scales.
-Quantify disturbance impacts and recovery trajectories, and develop rigorous modeling frameworks to test hypotheses about forest resilience and disturbance interactions.
-Collaborate across multiple disciplines, publish in leading journals, and present at major conferences.
School:
Qualifications
Basic qualifications (required at time of application)
Applicants must have completed all requirements for a PhD (or equivalent) in ecology, quantitative ecology, forest ecology, landscape ecology, or a related field, except the dissertation, at the time of application.
Additional qualifications (required at time of start)
Ph.D. in ecology, quantitative ecology, forest ecology, landscape ecology, or a related field. Ph.D. must be in awarded by the start of the position.
Preferred qualifications
-Publication record in relevant areas
-2-3 years of experience at the post-doctoral level
-Excellent quantitative and modeling skills including advanced coding in R and/or Python
-An interest and track record in publishing in top academic journals
-Familiarity with forest ecology and disturbance interactions
-Experience in big data analyses and developing reproducible workflows
-Proficiency using GitHub and coding languages, and other modern data management, sharing, and presentation interfaces
-Ability to work independently and collaboratively across disciplines
-Strong writing, communication, and organizational skills
Application Requirements
Document requirements