Postdoc Fellowship: Dynamic Energy Budget Modeling of Dreissenid Mussels in the Great Lakes

University of Michigan, Cooperative Institute for Great Lakes Research (CIGLR)
Ann Arbor, MI
Job Category
Post Doctoral Appointments
Last Date to Apply
A postdoctoral fellowship is available for a highly qualified individual to join the Cooperative Institute for Great Lakes Research (CIGLR, 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 successful candidate will work with a multidisciplinary team at CIGLR and the NOAA Great Lakes Environmental Research Laboratory (GLERL) to improve models of how invasive dreissenid mussels shape the lakes’ food webs. In particular, the candidate will develop a dynamic energy budget model for Dreissena that predicts net growth of mussels, including changes in energy storage, structural tissues, maturation, reproduction, and nutrient cycling. This model will build upon a rich history of studies conducted at CIGLR, GLERL and elsewhere related to mussel feeding and excretion, population dynamics, growth, and size structure. In addition to model development, the candidate will assist with planning and overseeing experiments required to parameterize the model.
Required Qualifications A Ph.D. in natural sciences or engineering with research experience in limnology, ecological modeling, or a similar field A strong background in deterministic modeling Familiarity with data analysis and visualization in a scripting environment using R, Python, or similar software Demonstrated ability to lead the development of manuscripts for refereed journal publication Strong communication skills and a demonstrated ability to work both as a team and independently Desired Qualifications Experience with dynamic energy budget models, or similar bioenergetic models Experience with developing parameter estimates from experiments or existing sources Experience conducting experiments with live organisms Demonstrated ability to analyze data, quantify uncertainty, and publish results in a timely manner
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