Two-Year Post Doctoral Fellowship in Forest Ecological Forecasting, Data Assimilation
A post-doctoral fellowship is available in the Laboratory of Tree-Ring Research (University of Arizona) to work on an NSF Macrosystems Biology-funded project assimilating together tree-ring and forest inventory data to analyze patterns and drivers of forest productivity across the interior western U. S. The aim of the project is to generate ecological forecasts of future forest ecosystem functioning, especially carbon sequestration, in the face of rising temperatures and evaporative demand. The approach is to leverage an existing, continental-scale ecological observatory network (the permanent sample plot network of the U. S. Forest Service’s Forest Inventory and Analysis Program [FIA]) and assimilate into it a new data stream: annual-resolution time series of individual tree growth from ~6,000 increment cores collected in the same plot network. The post-doc will be able to participate in all aspects of the project, with an emphasis on manipulating Forest Inventory and Analysis (FIA) census data, tree-ring data, and climate data, and scaling up an existing data assimilation workflow, with the opportunity to develop lines of research related to the themes of the lab based on their interests. The project will be co-supervised by Margaret Evans (Laboratory of Tree-Ring Research, University of Arizona), Justin DeRose and John Shaw (Interior West-FIA, Rocky Mountain Research Station) and statistical ecologists Andrew Finley (Michigan State University) and Mike Dietze (Boston University), along with the cyberinfrastructure support of NSF’s CyVerse. Applicants should have a PhD in ecology, forestry, or related field with strong statistical and computing skills, or a PhD in mathematics, applied mathematics, statistics, or a related field, with experience or interest in plant or forest ecology. The successful candidate will have a background and/or strong interest in hierarchical Bayesian models, data assimilation, dynamic linear modeling, ecological forecasting, uncertainty quantification, spatial statistics, dendrochronology, and/or computer science (e.g., writing MCMC samplers). Experience working with large datasets or databases, strong writing skills and associated publications in peer-reviewed literature, communication skills, and mentoring and collaboration skills are also strongly valued. The position is funded for two years, beginning as soon as December of 2018. Duties will be carried out at the Laboratory of Tree-Ring Research on the University of Arizona campus in Tucson, Arizona. The University of Arizona is a committed Equal Opportunity/Affirmative Action Institution. Women, minorities, veterans and individuals with disabilities are encouraged to apply. Situated an hour and a half from Mexico in the Sonoran desert and Sky Island region of southeastern Arizona, Tucson has an exceptionally low cost of living along with a wide range of opportunities for outdoor recreation and biological and cultural richness. One example is the recent designation of Tucson as a UNESCO World City of Gastronomy. Complete applications must include (1) a cover letter, (2) curriculum vita, and (3) names and contact information for three references, and should be submitted through the UACareers portal at https://uacareers.com/postings/32591. Applications will be reviewed until the position is filled. [https://uacareers.com/hr/assets/social_share.jpg]<https://uacareers.com/postings/32591> Research Associate I<https://uacareers.com/postings/32591> uacareers.com Two-Year Post Doctoral Fellowship in Forest Ecological Forecasting, Data Assimilation A post-doctoral fellowship is available in the Laboratory of Tree-Ring Research (University of Arizona) to work on an NSF Macrosystems Biology-funded project assimilating together tree-ring and forest inventory data to analyze patterns and drivers of forest productivity across the interior western U. S. The aim of the project is to generate ecological forecasts of future forest ecosystem functioning, especially carbon sequestration, in the face of rising temperatures and evaporative demand. The approach is to leverage an existing, continental-scale ecological observatory network (the permanent sample plot network of the U. S. Forest Service’s Forest Inventory and Analysis Program [FIA]) and assimilate into it a new data stream: annual-resolution time series of individual tree growth from ~6,000 increment cores collected in the same plot network. Outstanding UA benefits include health, dental, vision, and life insurance; pa
