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Post-Doctoral Research Associate
Position is with USDA, Agricultural Research Service, Sustainable Agricultural Systems Laboratory (SASL), Beltsville, MD. The duty station is at Department of Plant, Soil, and Microbial Sciences at Michigan State University (MSU), East Lansing, MI, under the mentorship and direction of Drs. Sieglinde Snapp (soil scientist/agroecologist) and Frederi Viens (statistician/data scientist).
Project Background: The research project’s aim is to evaluate crop rotation diversity effects on reducing producer risk and enhancing profitability at regional and national scales with three primary objectives:
1. Collate data from 13 long-term crop rotation systems projects into a national database for conducting meta-analyses and cross-site data syntheses.
2. Assess crop yield resilience to adverse weather conditions and determine sustainable options for reducing producer risks under future weather scenarios.
3. Evaluate how crop production risks are influenced by crop rotation length and identify significant gaps in our understanding of mechanisms contributing to multiple facets of cropping system performance.
Position Duties: Incumbent will focus on objectives 2 and 3 and work with a data manager responsible for objective 1. Using data from the participating 13 long-term cropping systems research projects, the incumbent will use statistical and simulation approaches to evaluate how cropping system diversity (CSD) interacts with biotic and abiotic factors to influence cropping system productivity and resilience, and how these interactions change across a range of climatic conditions and climate change scenarios. Probabilities will be used to evaluate hypotheses on production risks, and inform gaps in our understanding of mechanisms contributing to yield stability. Mentoring and Supervisory support will include the two senior MSU faculty members as well as other researchers in the project.
The incumbent should have experience in at least one of the following areas:
a) linear and nonlinear mixed models,
b) simulation modeling (APSIM, DSSAT, and possibly others),
c) computing probabilities (related to crop failures and high yield events) using one or several methodologies, which may include: finite mixture models of normal distributions; Bayesian hierarchical modeling with priors driven by external weather and climate projections; and multi-level hierarchical modeling for assessing shares of various risk components.
Ph.D. in Agronomy, Soil Science, Agricultural Engineering, Computer Science, Statistics, Applied Mathematics, or related discipline. Degree must be within 4 years of the entrance-on-duty date. Familiarity with database management, simulation models like APSIM, or DSSAT and programming skills are desirable. Background in statistics or other areas of data science is valued; additional basic training in these areas will be provided. Ability to communicate effectively with a diverse group of agriculture scientists at multiple locations across the US and Canada is essential. This position is limited to U.S. Citizens or Permanent Residents seeking citizenship as outlined in 8 U.S.C. 1324b(a)(3)(B).