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Field
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Iterative Algorithms: Optimization and Control.” About the Project The focus of the project is the analysis of iterative algorithms arising from time discretizations of nonlinear evolutions of various kinds
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, supervising the CLASS Honors Commons and classroom spaces to ensure optimal learning environments, and providing leadership and guidance to student support staff. The position also involves maintaining
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 11 hours ago
professionalism, enhance learning, and create personal and professional sustainability. We optimize our partnership with the UNC Health System through close collaboration and commitment to service. OUR VISION Our
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. Experience working with rodent models. Experience with mammalian cell culture. Experience with live-cell or in vivo microscopy. The optimal candidate will have a background in neuroscience or cell biology, as
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, analyze, and optimize new algorithms and system architectures for secure communication. Bridge theory and practice by developing simulation frameworks and/or contributing to experimental testbeds. Publish
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strategies (e.g. predictive or machine learning approaches) to improve performance and reduce costs. Collaborating with industrial partners on design optimization, life-cycle analysis, and business case
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deposition of functional topcoats for engineered textiles. Develop and optimize plasma deposition processes for tailoring material properties for specific textile functionalities. Characterize the deposited
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-processing pipeline for high-field MRI medical data (normalization, denoising, spatial registration) to optimize the quality and consistency of data used in analyses, and to facilitate the search
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optimization of pyrometallurgical flow sheets for the production of value-added materials or metals.The selected candidate will contribute to both fundamental understanding and industrial applications of thermal
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performs research at the interface of tissue, tumor, and vaccine immunology. Our overarching goal is to identify processes that optimize immune responses for application to the design of novel cancer