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Field
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to 1) digital twin technologies; 2) complex multiscale simulations and modeling; 3) large-scale hybrid machine learning and high-performance computing workflows; 4) tools and methods to allow productive
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Mississippi. Apply computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate
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, molecular biology, and in vivo models. Analyze and interpret data, integrating experimental and computational findings. Utilize bioinformatics tools and techniques to analyze high-throughput sequencing data
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equipment, computer resources, and yearly research support. Appointments are for one year with the possibility of renewal pending satisfactory performance and continued funding. Candidates are encouraged
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programming language (R, Python, or Perl) and experience working in Linux and/or high-performance cluster environments. A strong ability to perform analytical reasoning to extract biological insights from data
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Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances
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computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate hydraulic flood
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of scientific studies. Applicants with a strong background in computational biology are encouraged to apply. Job Description Primary Duties & Responsibilities: Information on being a postdoc at WashU in St. Louis
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programming language (R, Python, or Perl) and experience working in Linux and/or high-performance cluster environments. A strong ability to perform analytical reasoning to extract biological insights from data
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working in Linux and/or high-performance cluster environments. A strong ability to perform analytical reasoning to extract biological insights from data-driven approaches will be critical. In addition