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(SHM), physics-based modeling, and data-driven analytics to enable predictive, performance-based decision-making and improve infrastructure safety, resilience, and lifecycle performance. The candidate is
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learning, memory, and/or decision-making - Experience and/or interest in conducting developmental research - Experience with computational modeling approaches - Experience acquiring, analyzing, and
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Functionalization reaction on molecular model substrates Polymer synthesis by chemical post-modification Physico-chemical characterization of polymeric materials at different scales Writing of reports and articles
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models for high-temperature structural materials with applications in nuclear reactors and other energy systems. The candidate will collaborate with ANL staff to review, validate, and enhance methods
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biochemical datasets. A major focus will be on introducing new AI models to chart the chemical “dark matter” of the mammalian metabolome; examples of such models include large supervised or self-supervised AI
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the Large Language Models (LLMs). The successful candidate will work on pioneering research projects that push the boundaries of what AI can achieve, particularly in the domain of multimodal learning. You
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., encoder models, multi-modal embeddings, large language models), and mathematical modeling to map scientific concepts and their relationships, tracing how they change, merge, or diverge across time. Your
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to early-stage efforts focused on muscle biology and inflammatory disease. The successful candidate will apply their expertise in murine models and immunology to explore disease mechanisms, validate novel
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research under PI mentorship. Design, execute, and analyze translational studies focused on hereditary hearing loss and LOXHD1 dysfunction. Develop and characterize preclinical models, including mouse models
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on performance and availability of funding. We are seeking a highly motivated and qualified individual with a Ph.D. in Mechanical Engineering, Materials Science, Chemical Engineering, or a closely related