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learning from computer science or related areas and be proficient in Java, C, and Python programming. Familiarity with the source code of major open source machine learning software packages such as Weka
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translational researchers in the department as well as across the University of Florida campus. In particular, the Postdoctoral Researcher will lead and assist with data modeling, AI/ML methods, software package
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or experience in nontraditional research publication methods and collaborative notetaking software (e.g., Roam Research, Obsidian, Notion). ? Familiarity with cloud computing and machine learning techniques
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the project's objectives and contributing to the development of innovative biomanufacturing technologies. Responsibilities of Position: Design and analyze complex bioprocesses using process simulation software
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environmental and economic trade-offs are present in the corporate expansion of AI usage through enterprise software. How do these interests ensure their market share, control the regulatory environment, and show
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and willingness to have a societal impact; an empirical research focus and knowledge of quantitative content analysis and computational methods; proven proficiency in statistical software such as Stata
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or similar), demand modeling software (e.g., TransCAD, Cube, Visum) meso- and micro-scopic simulation (Vissim, SUMO, Synchro), Geographic Information Systems software (e.g., ArcGIS, QGIS) is required
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multimodal data (video, self-report, physiological sensors, automatic facial recognition software) that examines CER in multiple contrasting contexts. And (3) Advance educational theory and practice by
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Demonstrated experience with LS-DYNA or comparable commercial FEA software Proficiency in Python programming for scientific computing and machine learning applications Experience with machine learning methods
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to construct the models described above (where open-source code or implementations in standard software do not already exist). Work alongside the PI and other group members to develop more efficient methods for