30 machine-learning "https:" "https:" "https:" "https:" "https:" uni jobs at Indiana University
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research agenda using advanced quantitative methods—such as machine learning, computational modeling, big-data analytics, and wearable technologies—to study tourism, hospitality, and/or human performance
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research agenda using advanced quantitative methods—such as machine learning, computational modeling, big-data analytics, and wearable technologies—to study tourism, hospitality, and/or human performance
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the Artificial Intelligence program, developing courses for the traditional classroom setting, computer labs and for online education; help setting program and specialization goals, developing and continually
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for Indiana and beyond. Learn more about the new department at https://luddy.indianapolis.iu.edu/departments/cs/index.html . Review of applications will begin immediately, therefore qualified applicants
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are expected to teach at the undergraduate and graduate levels, and collaborate across disciplines to address real-world data challenges. Example areas include, but are not limited to: Machine learning and deep
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to submit their application as soon as possible Research expertise in the following areas of computer science will be considered: Fundamentals of Artificial Intelligence and Machine Learning, Robotics
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track) positions to teach Communication Skills (business presentations and/or business writing) courses to undergraduates in the Communication, Professional, and Computer Skills area (CPCS), effective
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of clinical and health informatics, systems interventions, community participatory research, human-computer interaction, usability, mobile technology, bioinformatics and biomedical engineering. Indiana is home
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research Benefit Information: - IUSM and IUHMG is committed to providing inclusive benefits for eligible employees and their families. to learn more, click here: - IU – https://hr.iu.edu/benefits - IUHMG
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Indianapolis: IUSM is committed to being a welcoming campus community and we seek candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments