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Field
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datasets. EIDA combines recent advances in “analysis-by-synthesis” approaches in computer vision with the “visual” and “practical” turns in the history of astronomy, in a deeply interdisciplinary framework
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position will be placed in the ISOVIS group (http://cs.lnu.se/isovis/ ), led by Professor Andreas Kerren. The research group mainly focuses on the explorative analysis and visualization of typically large
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 23 hours ago
thaliana. Techniques planned: * Imaging cytometry for quantitative analysis of subcellular metabolite localization. * High-throughput CRISPR screening. * Transcriptome data generation and analysis. * Cloning
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analysis and visualization of typically large and complex information spaces, for example in biochemistry, humanities, or software engineering. Our vision is to attack the big data challenge by a combination
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oceanography with demonstrated strengths in large data analysis, multivariate statics, and acoustic theory, and optical theory. The position will be primarily located in the Lewes Campus of the University
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experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
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. Organizational Status Reports to the Principal Investigator. Work Performed Conducting fMRI data preprocessing and analysis, the work is routine in nature or performed according to established protocols Creating a
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experiments, natural language processing, and network modeling. Experience mentoring students. Expertise in Python and TensorFlow. Proficiency in R, SQL for data analysis and visualization. Preferred
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big data and AI at its core. A central goal of the project is to build a foundation model of the visual brain—a “digital twin” that captures neural activity and intelligent behavior at unprecedented
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experiments, natural language processing, and network modeling. Experience mentoring students. Expertise in Python and TensorFlow. Proficiency in R, SQL for data analysis and visualization. Preferred