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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 6 days ago
lifecycle (version control, documentation, and testing) Research experience with astronomical data analysis and large-scale astronomical survey datasets Special Physical/Mental Requirements Ability to sustain
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large
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required Working knowledge of all aspects of planning and deploying an AI project, including initial problem specification, data gathering and analysis, model creation, and implementation Strong
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to the ongoing research in Prof. Marcotti’s laboratory by designing, developing and performing experiments, data analysis and disseminating the findings by writing articles and by presenting findings
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both qualitative and quantitative data and coordinating findings across data sources. The postdoctoral research associate will have opportunities to publish emerging findings from these research studies
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training students Maintain analytical equipment Cooperate with other large collaborative projects of the research group Qualification: Required: A diploma and a PhD in microbial natural product chemistry
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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of the extracts; LC-MS and bioactivity-guided isolation and structure elucidation of the purified metabolites Supervising and training students Maintain analytical equipment Cooperate with other large collaborative
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to study the interaction between behavioral goals, life experience, and sensory processing. Our experiments merge a range of approaches, including large-scale multi-area in vivo neural recording, wireless
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, transportation, and more, with a special emphasis on grid resilience assessments and equity analysis. You will have the opportunity to creatively use interdisciplinary methods from computational data science