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Field
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to the effective date of appointment with a minimum of one year eligibility remaining. • Experience in computational data analysis and scientific programming (e.g., Python, MATLAB, or similar). • Demonstrated
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• Experience in molecular biology techniques • Experience in computational data analysis and scientific programming (Python, R, MATLAB) • Experience in connectome analysis • Experience in molecular dynamics
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to design and operate catalytic and/or electrocatalytic reactors Ability to synthesize and characterize materials Ability to model and analyze process performance Proficiency in Python or an equivalent
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reactors Ability to synthesize and characterize catalysts Proficiency in Python programming Ability to apply machine-learning methods to scientific data Ability to work independently and across disciplines
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
Epidemiology, Biostatistics, or a related field Required Qualifications, Competencies, and Experience * Proficiency with R, SAS, or Python * Quantitative experience in epidemiology and statistics * Research
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Qualifications MD or Ph.D. in Basic Science, Health Science, or a related field. No experience required. Preferred Qualifications Has strong programming (Python and R) skills and molecular biology background
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programming/coding skills (e.g. Matlab, Python), and are quality scientific writers. Funding is available for up to two years, with renewal for year two based upon performance in year one. This position is part
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molecular profiling platforms. Experience with data analysis software such as GraphPad Prism, R, Python, or similar platforms. Other Requirements: This position is onsite. The work is performed on-site
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completed within the last 0-5 years)in chemical engineering, materials science, industrial engineering, or related fields. Knowledge of Python, JavaScript, Microsoft Excel and other computer programming
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structuring of river systems. Familiarity with software and tools for simulating river network dynamics, such as R, Julia, Python, or GIS-based hydrological modeling platforms. Ability to integrate physical