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), bioinformatics, or theoretical ecology (modeling) research in peer-reviewed scientific journals ● Experience in programming in R, Python, or a similar programming language. ● A demonstrated commitment to
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fitting, parameter estimation, and model comparison Proficiency in at least one scientific programming language (e.g., Python, MATLAB, R) Demonstrated ability to work independently while collaborating
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 17 days ago
black-box prediction; scientific computing (e.g., Python); and peer-reviewed publications. Experience in all areas is not expected; a strong foundation in the underlying physics matters most. Point
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modeling and environmental data analysis, such as Python, MATLAB, Fortran, C/C++, or comparable languages and tools. Ability to work with large datasets and to integrate these data with acoustics models
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experiments. Utilize laboratory techniques that support hypothesis-driven neuroscience research. Analyze and Interpret Data Process, interpret, and visualize scientific data using tools such as R, Python
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desirable For computationally focused candidates: proficiency in a scripting language (e.g., Python, R) and experience analyzing next-generation sequencing data; experience with statistical modeling
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statistical software & machine learning (e.g., R, Python, SAS, or STATA). Experience working with large population dataset (e.g. EHR, claims data). Background in health informatics, population health research
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-resolution microscopy such as STORM, and longitudinal live-cell imaging. Transcriptomic analysis, including bulk RNA sequencing or single-cell RNA sequencing. Computational analysis in R or Python
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pharmacoepidemiology and healthcare analytics experience (Medicaid, Medicare, commercial, and EHR populations). Proficiency in R, Python, and SQL. City: New Brunswick State: NJ Location: Rutgers University - New
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scenarios. The researcher will develop profit–risk–environment tradeoff products, build reusable R/Python workflows, and publish and present results in collaboration with USDA-ARS and university partners