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languages like Python or C, and or developing and/or using computational methods for analyzing large datasets. Demonstrated experience in developing computational algorithms for solving problems, preferably
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(NLP) algorithms applied to electronic health records (EHR) to understand cannabis-related harms in aging PWH and people without HIV. The position will entail collaborations with several investigators
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Posting Information Posting Details Department Computer Science - 318400 Posting Open Date 06/16/2026 Application Deadline 09/14/2026 Open Until Filled No Position Type Postdoctoral Scholar Position
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analysis and interpretation of multidimensional microscopy datasets, integrating image processing, spectroscopy, diffraction, and computational methods to extract meaningful scientific insights. 3) Lead the
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Associate will contribute to a research program focused on driven quantum systems and their applications to quantum information science, particularly in superconducting circuit platforms. This work includes
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designing, executing, and analyzing current or future quantum simulations at the intersection of subatomic physics and quantum information science. The successful candidate will also lead peer-reviewed
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Debanjan Chowdhury. The Postdoctoral Associate will contribute to a research program focused on driven quantum systems and their applications to quantum information science, particularly in superconducting
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will contribute to high-impact projects, including: 1. Developing and validating algorithms that extract data from the Epic EHR (e.g., large language models) via comparison with manually extracted data
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the essential functions of the job as outlined in the position description. Preferences Strongly Preferred Skills: ● Solid background and working knowledge of computer science fundamentals including algorithms
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between various imaging modalities and multi-omics during aging and development. • Implementing computationally intensive algorithms on high-performance computational clusters