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Experience with machine learning, data mining and data assimilation is a plus Knowledge of git, docker, kubernetes, and/or metadata is a plus Ability to work within a team Excellent interpersonal and
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chamber development, and (3) field studies in mining environments. Perform advanced sample analysis, validate measurements, and document findings. Process and analyze research data to generate meaningful
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. For more information about working at the University of Arizona and relocations services, please click here . Duties & Responsibilities Conduct independent research on (1) polymer self-assembly and
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Experience: Experience in advanced analytical theories, predictive modeling, data mining, and data extraction. Experience in clinical and health services research. Expertise in using various statistical
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-motivated and have strong oral/written communication skills. Preferred Qualifications: Previous experience with prostate cancer, epigenetics, bioinformatics data mining, and cancer xenograft mouse models is
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, modeling and analysis, integrating diverse data sets to identify global risks affecting sourcing strategies. In this role you will: Conduct and contribute to research and model development to enhance
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
Posting Information Posting Details Department Neurology - 411801 Posting Open Date 05/16/2025 Application Deadline Open Until Filled Yes Position Type Postdoctoral Scholar Position Title Post-Doc
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performed by graduate students, and mining existing data sets that will contribute to the body of knowledge in asphalt materials. • Write technical reports that summarize multi-faceted research projects
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that integrate simulation, machine learning, and data analysis. Numerical optimization methods (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms
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weather instrumentation data analysis (lidar, radar, weather station, radiometer, sonde, cameras) Experience with mining and cleaning “big” observational data and/or online databases Experience with