17 learning-analytics "Computer Vision Center" Postdoctoral positions at Virginia Tech
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learning for internet of things, or related areas. ● Strong analytical, problem-solving, and programming skills. ● Written communication skills demonstrated through peer-reviewed publications, technical
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of packaging designs for high-value product distribution. The ideal candidate will bring strong analytical and experimental skills to address challenges in packaging dynamics, product protection, and sustainable
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. The successful applicant will work closely with ORI Information Technology (IT), Electronic Research Administration (ERA), and Data & Analytics teams to develop and share best practices, understand available data
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, and use advanced analytical techniques on product identification. Required Qualifications - Recent Ph.D. in organic synthesis, asymmetric catalysis or an appropriate related field. - Experience in
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applications. The ideal candidate will possess strong analytical and experimental skills to address complex challenges related to packaging dynamics, product protection, and sustainable material performance
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analytical stills, including the use of statistical software (R, JMP, SAS, etc.) Preferred Qualifications N/A Overtime Status Exempt: Not eligible for overtime Appointment Type Restricted Salary Information
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peer-reviewed publications or preprints. • Must wear protective/safety protection with the ability to stand, stoop, bend, and walk for a long periods. Preferred Qualifications • Strong analytical and
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-learning methods, knowledge-graph and ontology-based scientific data infrastructures, and agentic workflows for autonomous hypothesis generation, mechanistic exploration, and design of catalytic systems
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that transforms current AI for Science paradigms focusing on multidisciplinary applications in biomolecular modeling and design, leading to a step-change in Scientific Machine Learning (SciML). They will be
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that can be repurposed for viruses with pandemic potential. This includes working closely with computer scientists to utilize published “omics” datasets and machine learning approaches to identify FDA