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Postdoc Fellowship - HABs statistical modeling

Expiration Date: 

Job Opening: Postdoc Fellowship - HABs statistical modeling
Cooperative Institute for Great Lakes Research
School for Environment and Sustainability
University of Michigan


A postdoctoral fellowship is available for a highly qualified individual to join the Cooperative Institute for Great Lakes Research (CIGLR, 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.

Required Qualifications

A Ph.D. in limnology, ecological modeling, or a similar field, with a strong background in statistical modeling is required. Familiarity with data analysis and visualization in a scripting environment using R, Python, or similar software. Strong communication skills and a demonstrated ability to work both as a team and independently, as well as lead the development of manuscripts for refereed journal publication.

Desired Qualifications

Preference will be given to candidates that have experience with contemporary statistical modeling approaches (Bayesian networks, causal analysis, hierarchical models, random forests, model averaging), including experience with water quality modeling and nutrient load estimation. Preference will also be given to candidates with a demonstrated ability to analyze data, quantify uncertainty, and publish results in a timely manner.

For more information and to apply:

The application deadline is August 14, 2022.