To apply, applicants should prepare the following materials:
Cover letter describing your qualifications related to the position and research accomplishments
Contact information for three professional references
Two representative publications
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.
The successful applicant’s appointment will be with CIGLR, which is part of the University of Michigan’s School for Environment and Sustainability located in Ann Arbor, Michigan. CIGLR is a collaboration between the University of Michigan and NOAA that brings together experts from academia and government research labs to work on pressing problems facing the Great Lakes region. The fellow will spend the majority of their time at the NOAA Great Lakes Environmental Research Laboratory in Ann Arbor and work in close collaboration with colleagues at the University of Michigan.
The University of Michigan is consistently ranked among the top American public research universities, and Ann Arbor is routinely ranked as one of the best places to live in the U.S. due to its affordability, natural beauty, preservation of wooded areas, vibrant arts program, and lively downtown.
This position offers a highly competitive salary plus benefits. The initial appointment is for one year, with opportunity for extension based on performance, need, and availability of funds.
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.
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.
Job openings are posted for a minimum of seven calendar days. The review and selection process may begin as early as the eighth day after posting. This opening may be removed from posting boards and filled anytime after the minimum posting period has ended.
The University of Michigan is an equal opportunity/affirmative action employer.
Internal Number: 181404
About University of Michigan - Ann Arbor
A great university is made so by its faculty and staff, and Michigan is recognized as one of the best universities to work for in the country. The Michigan culture is known for engaging faculty and staff in all facets of the university to create a workplace that is vibrant and stimulating.For two consecutive years, the Chronicle of Higher Education has placed U-M in its "Great Colleges to Work For" survey. In particular, the university earns high marks for strong relations between faculty and administrators, a collaborative system of governance, strong pay and benefits, and a healthy work/life balance.