University of Michigan – postdoctoral research fellow position

The Cooperative Institute for Limnology and Ecosystems Research (CILER) at the University of Michigan’s School of Natural Resources and Environment (SNRE), in partnership with NOAA-GLERL, is seeking to fill a postdoctoral position focusing on application of probabilistic and Bayesian modeling techniques to quantify uncertainty in empirical- and process-based models, and to propagate that uncertainty into water quality forecasts.

Projects are likely to include, but may not be limited to, quantifying uncertainty in fecal indicator bacteria (FIB) measurements and forecasting FIB and pathogen-based beach water quality standards in the Great Lakes region, with opportunities to participate in sample collection. The ultimate goal of this research is to provide robust decision support tools for regional beach managers through the USEPA-administered Great Lakes Restoration Initiative.

The physical work location will be at NOAA’s Great Lakes Environmental Research Laboratory (GLERL), 4840 South State Road, Ann Arbor, MI 48108. The fellow will work with a team of scientists from University of Michigan and NOAA, including Dr. Eric Anderson (eric.j.anderson@noaa.gov), Dr. Andrew Gronewold (andrew.gronewold@noaa.gov), and Dr. Craig Stow (craig.stow@noaa.gov).

Interested applicants should submit a cover letter and CV via the University of Michigan Career website http://umjobs.org/job_detail/92392/research_fellow.

The position will remain open until filled.

Qualifications:

The incumbent will have a minimum of a Doctorate degree in Environmental Sciences, Mathematics, Statistics, or similar degree, with experience using statistical modeling software packages (including, for example, R and BUGS) and encoding Bayesian MCMC routines. Excellent communication and problem-solving skills with the ability to independently manage and prioritize workloads.

Expected duties include:

• Quantify uncertainty in empirically-based model parameters using Bayesian MCMC (or similar) procedures, and express uncertainty in model-based predictions including expected water quality standard violation frequency.
• Explore spatiotemporal variability in nearshore water quality based on “in situ” measurements and assess relationships with hydrodynamic model simulations.
• Quantify uncertainty in conventional watershed pollutant fate and transport models using Bayesian MCMC (or similar) procedures.
• Publish results in refereed literature.

Larissa L Sano, Ph.D.
Researcher & Program Officer
UM – Cooperative Institute for Limnology & Ecosystem Research UM – Water Center
ph: 734.763.5014