19 machine-learning "https:" "https:" "https:" "https:" positions at University of California Los Angeles
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proposals. Responsibilities Develop, implement, and evaluate new statistical and machine learning methods aligned with the two themes above. Lead and co-author manuscripts in statistical, machine learning
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in the new field of machine learning is favored, as is the ability to work collaboratively with other faculty and clinical colleagues and an interest in collaboration with surgical colleagues
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for research. (Required) Knowledge of medical terminology. (Preferred) Demonstrated computer skills/abilities: excellent word-processing skills (Required) Willingness to learn to support study procedures
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for course management. Your application is only complete and available for review when you receive an auto-generated confirmation from the recruitment system. To apply, please visit: https
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are template based and all US measurements are auto-populated into templates for increased accuracy and efficiency. Fourteen fellowship-trained, subspecialized expert faculty perform both image interpretation
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) Demonstrable experience/training in the concurrent use of fMRI and EEG Demonstrable experience/training on the use of phenomenological measures Experience with Machine Learning methods to assess aspects
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applied statistics, data science and analytics, methods for analyzing big data and machine learning, and a commitment to excellent teaching. Adjunct professors are expected to continue scholarly work
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the University or outside. (Required) Demonstrated excellent skills in various computer programs, including spreadsheet and database software. Ability to learn new programs quickly and thoroughly. (Required
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understanding of activity, role and purpose of thinking in an age of machine learning, cognitive and neural engineering and enhancement. Approaches that emphasize diverse forms of thought (animal cognition
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are template based and all US measurements are auto-populated into templates for increased accuracy and efficiency. Fourteen fellowship-trained, subspecialized expert faculty perform both image interpretation