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Field
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Postdoc in "Navigating uncertainty: Planning marine protected areas in a changing Southern Ocean"...
statistics and the ability to apply quantitative analysis to ecological data A strong background in programming (preferably in R), including data manipulation, statistical analysis, and spatial modelling and
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programming language (R, Python, or Perl) and experience working in Linux and/or high-performance cluster environments. A strong ability to perform analytical reasoning to extract biological insights from data
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with large-scale human omics (preferably related to neurodegeneration) studies is expected. Preferred Qualifications: Skilled in at least one programming language (R, Python or Perl) and experience
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(SCIPP) at the University of California, Santa Cruz invites applications for a postdoctoral scholar position in the Fast Sensors R&D group at SCIPP. As a leading academic facility for R&D in sensor and
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. • Demonstrated ability or potential to lead graduate student research. • Experience with Matlab, R, Python, C++, or Julia. • Experience collecting data from human subjects. Exempt: Not eligible for overtime
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R documented excellent and intuitive graphical presentation of data analyses for presentation and publication excellent English language skills (speaking and writing) comprehensive presentation
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models using software such as GAMS and Python. Econometric modeling skills using statistical software (e.g., Limdep, Stata, EViews or R). Strong programming skills with R and Python. Excellent oral and
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methods to compare the new designs and techniques with existing ones in binary, continuous, and time-to-event outcomes. The Postdoc Associate will help develop R/SAS software for the proposed statistical
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the following areas: Performing and/or analyzing functional genomics experiments Competence with Unix environment, R, Python, high performing cluster Familiarity with machine learning Have taken coursework in
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practices ● Fluency in programming languages such as R and Python. ● Demonstrated ability to make reproducible code for cleaning, integrating, and modeling spatial/temporal data from multiple sources, spatial