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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu/silverlab/ . Conduct independent research activities under the guidance of a faculty mentor in
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computational modeling to identify bacterial strains and metabolites that promote or hinder probiotic establishment. By combining multi-omics data with systems biology and machine learning approaches
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the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
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physiology is an asset Experience in statistics and data analysis of large data sets (including time series analyses and machine learning) Good programming skills Very good communication and interpersonal
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streaming and batch processing. These efforts provide the foundation for advanced analytics, machine learning, and AI applications. The IDE Research School guides PhD researchers by offering a platform for
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for Communication Systems carries out research, undergraduate and postgraduate education in communications engineering, statistical signal processing, network science, and decentralized machine learning. Welcome
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monitoring. Familiarity with computational image analysis, scripting (Python, MATLAB), or machine learning–based image workflows. Experience with method development, imaging assay optimization, or pipeline
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at https://puwebp.princeton.edu/AcadHire/position/40281 and submit a current curriculum vitae, research statement, and a cover letter. Contact information for three references is required. To learn more
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assimilation, machine learning, and optimization techniques. Experience in student mentoring. Publications in leading journals within the field. Preferred Qualifications PhD in Environmental Modeling. More than