Postdoctoral Fellowship: Statistical Modeling of Harmful Algal Blooms: Michigan

Agency
University of Michigan, Cooperative Institute for Great Lakes Research (CIGLR)
Location
Ann Arbor, MI
Job Category
Post Doctoral Appointments
Salary
Highly competitive salary with full benefits
Last Date to Apply
04/07/2022
Website
https://careers.umich.edu/job_detail/213219/postdoctoral-fellowship-statistical-modeling-harmful-algal-blooms
Description
A postdoctoral fellowship is available for a highly qualified individual to join the Cooperative Institute for Great Lakes Research (CIGLR, https://ciglr.seas.umich.edu/). The successful candidate will work with the harmful algal bloom (HAB) team at the NOAA Great Lakes Environmental Research Laboratory (GLERL) to improve our ability to predict algal bloom development and impact on human health in the Great Lakes. In particular, the candidate will develop new statistical modeling approaches emphasizing the probabilistic aspects of algal growth and toxicity, and incorporate approaches for rigorous model skill assessment and uncertainty analysis. In addition to statistical model development, the candidate will assist with field planning, experimental design, data analysis, and the development and transition of research products to application. Postdocs will be expected to maintain strong records of scholarly publication, as records of presentation at scientific conferences and public meetings.
Qualifications
Required Qualifications A Ph.D. in ecology or environmental science Strong background in statistical modeling Familiarity with data analysis and visualization in a scripting environment using R, Python, or similar software Demonstrated record of contributing to manuscripts for refereed journal publication Desired Qualifications Experience with contemporary statistical modeling approaches (Bayesian networks, causal analysis, hierarchical models, random forests, model averaging) Experience with water quality modeling and nutrient load estimation Demonstrated ability to analyze data, quantify uncertainty, and publish results in a timely manner
Contact Person
Casey Godwin
Contact eMail
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