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engineering is considered a plus. The lab has routine access to Krios and FIB-milling instruments. 2) Genome editing: Projects involve developing novel genome editing tools and optimizing in vivo delivery
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on these topics. Basic Qualifications Ph.D. in Energy/Environmental/Natural Resource Economics or related Applied Economics discipline with a strong quantitative focus. Experience with quantitative optimization
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
Vision is to use scientific discovery to ensure optimal health through individualized nutrition. Everyone who works at the NRI supports this hopeful vision. Working at the NRI means doing good
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simulation models to analyze, predict, and optimize water distribution networks. Engage in state-of-the art research on lithium supply chain dynamics, forecasting potential bottle-necks, and proposing
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). Designs experiments employing immunologic assays and GVHD/GVT models to evaluate invariant natural killer T cell function. Optimizes transgene delivery to engineer invariant natural killer T cells
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Responsibilities Research: - Conduct research in AI reasoning, semantic modeling, and knowledge graph development. - Develop and optimize graph databases for structured knowledge representation. - Apply neural
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activity and people positively impact the world. Like the city we call home, UB is distinguished by a culture of resilient optimism, resourceful thinking and pragmatic dreaming that enables us to reach
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culture and optimization and large-scale protein expression and purification as well as biochemical analysis. Projects will enjoy fantastic access to a state-of-the-art in-house Cryo-EM facility featuring a
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of decarbonization policy pathways for electric power systems. Desired experience includes optimization and simulation of electric power systems under uncertainty, programming in Python, Julia, and/or MATLAB, and
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response Demonstrated expertise in process development/optimization for macro-scale deformation in AM Experience with multi-physics simulations on high performance computing (HPC) and maching learning (ML