Postdoc fellowship: Statistical modeling of harmful algal blooms: Ann Arbor, MI

Agency
University of Michigan, Cooperative Institute for Great Lakes Research (CIGLR)
Location
Ann Arbor, MI
Job Category
Post Doctoral Appointments
Salary
54,000-56,000
Last Date to Apply
08/14/2022
Website
https://careers.umich.edu/job_detail/219740/postdoctoral-fellowship-statistical-modeling-harmful-algal-blooms
Description
Summary 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 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.
Contact Person
Mary Ogdahl
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