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Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data
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their own vehicle and find their own housing for the duration of the position. QUALIFICATIONS Knowledge of computer programming language(s) for data analysis and visualization Knowledge of spreadsheet
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suite. Contribute to and guide others in producing and delivering communications tools (presentations, visual communication material, and information graphics, white papers, etc.) aimed at collaborators
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Cornell University, History of Art and Visual Studies Position ID: Cornell-History of Art and Visual Studies-POSTDOC [#31620, WDR-00056959] Position Title: Position Type: Postdoctoral Position
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, meteorology, physics, earth observation. Expertise in working with climate modelling, ideally experience with model development with FORTRAN. Good programming skills in a data processing and visualization
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, including within the electronic health record; (3) Data visualization with report and manuscript preparation. Qualifications Requirements: Bachelor’s degree or higher. At least one year of experience in
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institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences, and
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for machine learning Competent in soft skills / general personality traits e.g. information visualization, interaction design We regret to inform you that only shortlisted candidates will be notified. Hiring
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ideological and theological messages, especially in contexts of low literacy and absence of media. Using the most comprehensive dataset of visual art in Italian churches, combined with computer vision methods
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 17 days ago
team and may help in designing and developing visualizations and dashboards. Results of the research may inform updates to data considerations for internal regulatory science databases. Additionally, you