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. - Strong expertise in multiscale/multiphysics modeling relevant to catalysis, and experience with machine learning models. - Deep understanding of reaction kinetics, thermodynamics, and structure-reactivity
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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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. The candidate will apply a suite of statistical and physical models for integrating observations of different accuracies, improving predictions of future hazards. The work will further include research, writing
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systems. • Develop and implement predictive models for packaging performance using machine learning approaches and physics-based simulations. • Investigate logistics optimization strategies for packaging
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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
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. • Possess molecular virology or protein biology skills. • Should be enthusiastic and dedicated, have the ability and interest in learning new techniques; must be able to follow verbal and written instructions
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culture and/or rodent behavioral models • Strong organizational skills and the ability to manage experiments independently • Effective written and verbal communication skills Preferred Qualifications
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. Responsibilities: • Design and conduct experimental research on paper-based materials for food packaging applications. • Develop predictive models for packaging performance using machine learning and physicochemical
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models and machine learning techniques for kinetic equations arising from plasma and neutron transport. The position will be based at Virginia Tech’s campus in Blacksburg, VA. The postdoc will have a