11 machine-learning "https:" "https:" "https:" "https:" "U.S" uni jobs at Indiana University
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information exchange (HIE) Natural language processing in clinical/biomedical domains Mobile health, digital health, human–computer interaction in health Learning health systems, community health informatics
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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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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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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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) research in the areas of clinical and health informatics, systems interventions, community participatory research, human-computer interaction, usability, mobile technology, bioinformatics and biomedical
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) research in the areas of clinical and health informatics, systems interventions, community participatory research, human-computer interaction, usability, mobile technology, bioinformatics and biomedical
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to incorporating generative AI (and related tools) to enhance student learning. Responsibilities Full-time lecturer-track faculty commit to a balanced workload of teaching and service, which includes: Teach graduate
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therapeutics, wearables, artificial intelligence (AI) and machine learning (ML), public health surveillance systems, and virtual/augmented/extended reality. Health conditions of interest are also broad and may
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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