Growth Model Users Group
Job Listings
(posted as a courtesy and service to our members)
Valuations Inventory Analyst – Portland-based timber management firm seeks a highly motivated individual for a Valuations Inventory Analyst position in Portland, OR. We’re looking for talent who supports and appreciates our desire to work hard and serve up an excellent product as well as someone who enjoys working with a team.
A BS degree in Forestry is required; A Master’s Degree in a quantitative area of Forestry, preferred. Please refer to www.campbellgroup.com for more information and to apply. The Campbell Group is an equal opportunity employer and offers competitive salary/benefits. Resumes will be accepted until the position is filled.
University of Idaho
College of Natural Resources
Department of Forest Resources
PO Box 441133
Moscow, Idaho 83844-1133Phone: 208-885-7952
Fax: 208-885-6226
Post Doctoral Fellow Position:
Forest Biometrics and ModelingAnnouncement: We are seeking a successfully completed PhD student to work with an interdisciplinary team of scientists to analyze a large forest stand database (>10,000 plots) from private, state and federal forest land holders in the Intermountain West and to develop geospatial models for assessing site quality. The project is funded through the Intermountain Forest Tree Nutrition Cooperative (IFTNC) www.cnr.uidaho.edu/IFTNC/.
Background: Forest land owners must evaluate the influence of numerous site factors in order to make sound economic and ecological forest management decisions. Precise knowledge of site characteristics controlling forest stand density and productivity has been elusive because of complex interactions among various climatic and edaphic components. The IFTNC Site Type Initiative (STI) is designed to identify and model growth promoting factors that determine forest site carrying capacity and stand productivity. Mensurational and landscape data will be obtained from participating cooperators to build a comprehensive database of stand growth conditions across the Inland Northwest. Self-thinning boundary line analysis will be used to assess the impact of site factors on site carrying capacity for managed species. Statistical models will be developed to assess the integrated value of static and dynamic site variables contributing to forest site quality. Principal factors that define nutrient supply, such as rocks and soils are expected to be static, while those defining moisture and temperature will be much more dynamic. Inclusion of dynamic variables will allow any developed models to respond to actual or predicted changes in climate conditions. Model variables will be dynamically linked to corresponding geospatial data layers, which will allow geospatial tools to be interactively linked to best and most current data. Statistical and geospatial models will be delivered to IFTNC members for incorporation into their decision support systems.
Responsibilities: The post-doctoral fellow’s research will have a focus on developing and validating geospatial biometric models for site quality which will be distributed to regional land managers. Additional biometric projects will include analyzing 30 yrs of IFTNC legacy data sets in forest nutrition and productivity.
Funding: Salary is $36,000 plus health and retirement benefits. Term is for one year, with potential for annual renewal depending on Fellow’s interest and continued project funding.
Qualifications: The post-doctoral fellow should have strong statistical skills in current biometric modeling methods; familiar with stochastic frontier analysis in its application to forest stand density models; multiple adaptive regression splines; geographically weighted regression analysis; be familiar with geospatial analysis tools (GIS, remote sensing, GPS); methods for assessing forest productivity; and knowledgeable of above and belowground processes controlling variation in productivity and stand density in forested systems. Desire to conduct field work in managed forests and strong verbal and written communication skills are critical. Successful candidates will need to show evidence of statistical knowledge and an ability to publish research results in refereed journals. Applicants should have a PhD in a natural resource field: forest biometrics, forest management, forest ecology, or related fields
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