74 structural-reliability-analysis-"https:" Postdoctoral positions at Cornell University
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Function The Dillon Lab explores the role of animal-source foods in sustainable food systems. Our work elucidates how system structure mediates sustainability outcomes from management change and policy
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. Anticipated Division of Time Data collection, analysis, and interpretation (70%) The postdoctoral associate will design, execute, and interpret all experiments related to the project in collaboration with other
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). The candidate will also have the opportunity to develop a small outreach project through an ongoing collaboration with the Ithaca Sciencenter. Anticipated Division of Time Data collection, analysis, and
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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these
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: The selected candidate will: Design, fabricate, and characterize advanced MEMS/NEMS test structures and integrated devices using Cornell NanoScale Facility processes, working closely with collaborators across
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operation, feedstock pretreatment and characterization, analytical measurements by LC and GC; experimental design, statistical analysis, bioprocess simulation through kinetic modeling. Additional training in
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studies will also be considered. Computational methods, content analysis, surveys, and other methods can be used complementarily, but should not be the primary method. Viable path towards access
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and/or case-crossover analyses, and have a promising publication record. Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization
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health and well-being, cognitive aging and dementia, loneliness and social isolation, and integrative data analysis. Strong analytic skills such as structural equation modeling, multilevel modeling
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skills such as structural equation modeling, multilevel modeling, longitudinal data analysis, and/or categorical analysis (e.g., growth mixture modeling) using R, Mplus, and/or SAS. Preferred