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of the project, through development of the R package treepplr (www.github.com/treeppl/treepplr ). The work will include development and implementation of methods to test model adequacy, inference diagnostics
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utilization; to minimize our dependence on imported energy; and to enhance our national security. A research and development position is now open in the Materials Recycling R&D Group. A post doc position is now
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expertise in one or more of the following: software package development and maintenance in R; record linkage/entity resolution; data privacy techniques; large data processing and high performance computing
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learn new advanced analysis techniques (e.g., in Matlab, R, or Python) if relevant for the task. Write up research results in the form of journal articles. Participate in and co-arrange national and
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. Responsibilities include cleaning datasets from recently completed research studies, database management, conducting statistical analyses (SAS or R), review/verification and archival of programs for research
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using R for statistical analysis and in statistical modeling. Sound working knowledge of unix shell, high performance compute clusters and git. Willingness to learn and use a scripting language (e.g
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related discipline and have begun to establish a strong research profile, evidenced by a strong, well-cited publication record. Proficient in Python, R, BASH and/or other relevant programming languages, you
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or Perl, along with experience using shell environments (e.g., Bash) and statistical programming in R, is required. Entirely remote work arrangements (including for applicants residing outside of Arkansas
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or soon after Advanced training in econometrics, empirical archival methods, and causal inference methods Strong programming skills in Stata, SAS, R, Python, or similar software Experience in teaching
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groups/minorities would be an added strength. Preference will be given to applicants with knowledge of analyzing quantitative research data using advanced statistical tools (e.g., MPlus, R). Applicants