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Field
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integral equations Machine learning, especially the areas of optimization, learning theory, probabilistic modeling, deep learning, and high dimensional data analysis, as well as applications of scientific
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for an ultra-high vacuum synchrotron end-station using CAD software. - Run, optimize, and document data acquisition codes (LabVIEW and Python) - Aid NIST staff in developing plans
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: Follows NIH Postdoctoral guidelines and comprehensive benefits Start date: As soon as possible Core Responsibilities Develop and optimize protein purification protocols for key stress response factors
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variety of conditions that are not optimal growth conditions. The survival data of pathogens under these conditions (growth matrices + sub-optimal growth temperatures + antimicrobial stresses) will also be
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environmental benefits by the increasing use of recycled asphalt binders in new pavements. Under the guidance of a mentor, the participant will participate in the development and optimization of chemical
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to optimize soil NOx emissions and to understand the underlying environmental and human factors driving the flux variability. The position will report to Prof. Dien Wu (project lead) and is expected to meet
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one or more of the following areas: (1) modeling of infectious disease dynamics, (2) statistics, machine learning, and AI, or (3) operations research and optimization. Preference will be given
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challenges, including data analysis, hypothesis generation, and experimental design optimization. We seek a highly motivated postdoctoral candidate with expertise in AI, machine learning, and computational
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analytics and insights collection and reporting Support of medical congress and information booths Contributing to the execution of omnichannel and digital strategy within Medical Affairs to ensure optimal
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, complex hydrodynamics and hydroacoustics, data assimilation and multi-disciplinary optimization, and autonomy. We seek talented, motivated postdoctoral applicants from all areas of engineering, math