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& Automation: Strong Linux administration skills and experience with scripting (Bash, PowerShell, Python, or Ansible) and - Infrastructure as Code (IaC) tools like Terraform and Ansible. - Modern Technologies
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quantitative data and ideally historical data sources - Knowledge of descriptive statistics and statistical software (e.g. R, stata, Python) - Interest in economic sociology and political economy - Knowledge
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reports to publication. Researching new software solutions, such as R-packages, Python, Google colab, Jupyter notebook, ShinyApp, SAS PROCs or macros that might be useful to Statistical and Data Analytics
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professional experience or academic coursework. · Advanced degrees, scientific programming language(s) (Python, R, Matlab) and computer related skills (networking, Linux) are beneficial. · Bachelor’s Degree in
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molecular, cellular, or quantitative biology. Experience with microscopy, image analysis, computational analysis, or data visualization preferred. Exposure to programming or analysis tools such as R, Python
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Python programming. • Proven experience in writing scientific papers. • General knowledge of physical oceanography or climate dynamics. The selection committee will take gender balance within
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physics. Experience in surface science, crystallography, Python programming, and/or quantum chemistry will be considered an asset. Excellent communication skills are essential, with the ability to work and
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skills: Strong background in chemistry, physical chemistry, materials science, or condensed matter physics. Experience in data science, Python programming, high-performance computing and/or quantum
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or Python. Responsibilities include teaching two undergraduate courses in each of Lawrence’s three ten-week terms. The successful candidate will be the third statistics and data science faculty member in
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-performance computing, and Python-based data analysis. Equivalent combination of education/experience. Equivalent education and/or experience may substitute for minimum qualifications except when