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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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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 6 hours ago
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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field. Demonstrated experience with satellite remote sensing for large-scale vegetation or forest monitoring, including Landsat and/or Sentinel-1/2 time series. Strong proficiency in Python for geospatial
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and rigorously evaluating new machine-learning methods. • Excellent Python programming skills and strong hands-on experience implementing, training, and evaluating deep-learning models and research
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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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psychoneuroendocrinological and behavioral studies; advanced statistical and data analysis skill (preferably in Matlab, R, Python) and a track record of peer-reviewed papers of high ambition and quality (ideally published in
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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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computing environments, such as Python, R, or MATLAB; A strong publication record appropriate to the candidate’s career stage; The ability to work both independently and collaboratively in
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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
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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