Research Associate

Oregon State University
Corvallis, OR, United States
Position Type: 
Full-Time
Organization Type: 
University/Academia/Research/Think tank
Experience Level: 
Senior (10+ Years)
Degree Required: 
Doctorate

EXPIRED

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The Department of Forest Engineering, Resources and Management invites applications for a full-time (1.0 FTE), 12-month, fixed-term Research Associate position. Reappointment is at the discretion of the Department Head.

The Research Associate will develop spatial tools and models that relate aquatic water quality to landscape factors, as part of an agreement between Oregon State University (OSU) and the US Forest Service (USFS), and an interagency agreement between USFS and the US Environmental Protection Agency (EPA). The Research Associate will conduct research that improves understanding of how natural and anthropogenic watershed characteristics contribute to water quality at regional to national scales. The position will contribute to the mission of both the College of Forestry and Oregon State University by a) conducting distinctive problem-solving research, b) supporting a continuous search for new knowledge and solutions, c) educating and engaging practitioners and users of the world’s forest resources, and d) maintaining a rigorous focus on academic excellence.

The Research Associate will develop spatial tools and models that relate aquatic water quality—in streams, rivers, lakes, wetlands, and/or coastal waters—to landscape factors, in general, and forest health and wildfire, in particular. This will be done, in part, using and further developing EPA’s StreamCat (ftp://newftp.epa.gov/EPADataCommons/ORD/NHDPlusLandscapeAttributes/Strea...) and LakeCat (ftp://newftp.epa.gov/EPADataCommons/ORD/NHDPlusLandscapeAttributes/LakeC...) datasets, as well as other related landscape data. The Research Associate will conduct studies at regional to national scales. These studies will generally be based on a watershed unit, although other spatial scales and landscape units could be studied, including modeling of individual watersheds. Modeling may include empirical approaches, such as multiple linear and nonlinear regression, random forests, and neural nets, and may also include simulation or mechanistic approaches.