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Field
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& Maintenance Intelligence – System Autonomy," aiming to drive profound transformation in industrial manufacturing by breaking through key technologies in smart equipment, industrial software, and system
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, heterogeneous datasets and developing reproducible analytical workflows, including using LLMs for the same. Demonstrated software development practices, including documentation and version control. Skilled in
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safe and green way. The successful applicants will join and drive a number of fascinating projects on designing, implementing, optimizing, prototyping and testing innovative solutions (software, hardware
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, human-systems interactions, networks science, information, decision complex networks, dynamic data-driven application systems, the science of information computation and fusion, systems and software
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 13 days ago
machine learning and/or biomedical image processing. The ACM Lab is developing software to support AI-driven techniques to rapidly diagnose, track, and treat neurodisorders. The ideal candidate will have a
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at scale. Experience in HPC and associated software development for applications, middleware, and/or system software. Flexibility to adapt to diverse R&D projects and tasks. Effective communicator in both
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software [e.g., Xcalibur, Compound Discoverer, Skyline, TraceFinder]. Experience integrating multi-omics datasets (e.g., combining sequencing and metabolomics data). Experience with animal models of host
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mixed methods research on related topics, including surveys, interviews, focus groups is preferred, but not required. Must have expertise in using statistical software such as STATA, R, SAS or SPSS
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and software tools for visualizing and analyzing materials characterization data Develop novel, data-driven materials characterization workflows Advance understanding of process-structure-property
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popular software in genomics, including Galaxy and AnVIL . These software platforms help thousands of researchers each year solve their data analysis needs. As the size of data and number of files grows