35 machine-learning "https:" "https:" "https:" "https:" "https:" positions at Indiana University
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and photonics Neuromorphic computing, artificial intelligence and machine learning Quantum and 2D materials technologies & systems Micro and nanoelectromechanical systems (MEMS/NEMS) Electromagnetics
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, synaptic growth, brain network organization and connectivity, cognitive function) Using advanced neuroimaging and/or machine learning techniques to understand the connection between physical activity
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programs in areas such as Bioengineering, Computer Engineering, Microelectronics, Nanoengineering, and Robotics. This role offers a unique opportunity to elevate the department’s national and international
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years. For additional information on life in Indy: https://faculty.medicine.iu.edu/relocation About the IUSM: IUSM is committed to being a welcoming campus community and we seek candidates whose research
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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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, Neuroscience, or a related field by the start date. Demonstrated expertise in computational modeling of human behavior or computer vision / machine learning. Proficiency in Python, MATLAB, or R. Strong
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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