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cultural data. Experience with AI tools such as machine learning for sentiment analysis, data mining for social trends, or computational linguistics for linguistic analysis will be highly regarded
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conference presentations. - Knowledge of machine learning applied to clinical data. - Ability to communicate technical results to both specialized and non-specialized audiences. - Knowledge of advanced
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under the Department/Faculty/University. Applicants should possess a PhD degree in Computer Science, Computer Engineering, Information Systems, or a related field, and sufficiently demonstrate abilities
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benefits package, including health and life insurance, generous paid leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https
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; however, we are particularly interested in the following areas: AI/Data Science/Controls in Biomedical Engineering/Biomanufacturing (brain-computer interface, computer vision, machine learning, digital
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Genetics, Nature Neuroscience, and others. We are currently expanding efforts to build large-scale data commons for human complex disorders and to integrate emerging technologies such as AI to drive the next
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analysis and machine learning and experience in the analysis of diverse data types (clinical, genetic, imaging, digital, blood biomarker). Cultural and language competency and/or experience working with one
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accurate logs, write lab reports. 6. Input computer data, organize and maintain computer data records. 7. Generate graphs and diagrams of publishable quality. 8. Contribute to preparation of scientific
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beyond ITER with advances in HTS materials, manufacturing, sensors and machine learning. High current density cables are required for the design of compact HTS magnets in a fusion pilot plant (FPP
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translational research. Machine Learning, Statistical Modeling & Informatics Research Apply ML/AI approaches for pathogenicity prediction, phenotype clustering, or multimodal data modeling. Support translational