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modeling approaches. • Investigate interactions between food products and paper-based packaging materials across diverse product categories. • Collect, process, and analyze experimental data, including
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Job Description We are seeking a visionary researcher to lead the development of transformative global water and infrastructure datasets that underpin AI and process-based hydrologic models. You
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characterization of soft electronic systems, including stretchable conductors, liquid metal composites, multilayer soft circuits, or microfabrication techniques. - Research experience in adhesion science, including
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distribution systems. Current research initiatives include developing advanced vibration analysis methodologies for wooden packaging systems, predictive modeling for packaging performance, and optimization
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, sustainable materials, or engineering to contribute to our ongoing research on improving the hygrothermal performance, structural modeling, and durability of CLT, including hybrid and thermally modified wood
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people in the Northern Virginia region. This position will support several stakeholder-driven projects, with the overall goal of developing novel analytical and modeling approaches for PFAS source
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systems for accelerating computational catalysis and experimental design. The successful candidate will contribute to building AI-native frameworks that combine first-principles modeling, machine-learning
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. The position involves research in modeling and simulation of within-host virus dynamics, theoretical immunology and multiscale immune-epidemiological models of infectious diseases and teaching two classes per
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predictive modeling approaches to understand corrosion mechanisms and coolant/fuel chemistry in extreme conditions. The successful candidate will oversee corrosion-focused projects sponsored by industry and/or
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of causal machine learning and optimal policy learning. • Proficiency in other languages such as Stata, and/or Python modeling languages. • Research experience using Python. • Experience working with large