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  • National Institute on Scientific Teaching

University of Michigan

The University of Michigan is looking for an Intermittent Lecturer in the School of Environment and Sustainability to teach a course on Environmental Spatial Data Analysis. This posting will end on 7/12/19. Review of applicants will begin on 7/13/19.

To apply, submit a letter of application explaining your qualifications, teaching philosophy, and a current CV (in a single PDF file) to Letters of reference may be requested for the next level of consideration

The successful applicant will teach EAS 543 - Environmental Spatial Data Analysis. This half-semester course focuses on frequently used quantitative methods in spatial analysis and spatial statistics. Course topics include use of R in spatial analysis, assessment of spatial autocorrelation, spatial point pattern and cluster analysis, spatial interpolation including IDW and kriging, and regression in spatial analysis including logistic regression models. The course will focus around one or more example projects and students will use the above methods to address a set of integrated spatial questions about a real landscape/study site. The course format emphasizes hands-on lab work, and uses the R statistical package and ArcGIS Pro. This is a 1.5 credit course at 17% effort and will be offered in the second half of the 2019 Fall term, beginning October 29, 2019, Thursdays 10-12 in SEAS computer classroom.

Required Qualifications

Ph.D. in geography, natural resources and environment, or a related field with expertise in quantitative methods used in spatial analysis and spatial statistics; familiarity with natural resource and environmental applications; fluency with the statistical software R and ArcGIS; teaching experience in higher education.

Desired Qualifications

Experience in developing and leading hands-on sessions in a computer laboratory.Demonstrated expertise and experience with regard to the minimum and desired qualifications; evidence of teaching quality.

Learn more about the position here:

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