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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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Proteomic and metabolomics analysis; Biomarker identification through the use of machine learning approaches; and Multi-omics data integration with genomics, transcriptomics and methylomics data. Job
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Biomedical Engineering, Medical Physics, Electrical, Computer Engineering or a
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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data-intensive operations in scientific and AI applications. Investigate machine learning techniques to inform heuristic methods for routing optimization, bridging theoretical insights with practical
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Alexandria, Virginia. The focus of these positions will be on quantum computing, quantum algorithms, quantum learning, quantum error correction, and quantum fault-tolerance. The successful candidate will join
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include, but are not limited to, using the latest computational learning-driven approaches, including computational social science, foundation models and multimodal machine learning, to enhance
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journals and conferences. This role provides a unique opportunity to work with the world’s first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric
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, establishment of a seagrass farm, and monitoring of a large living shoreline project. In addition to research, the post-doctoral scholar will be required to teach a 4-5 week-long field course each spring semester
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postdoctoral associate positions, starting immediately. Dr. Liu has extensive experience in big data analytics, systems biology, probabilistic graphical models, causal inference and machine learning. Dr. Liu's