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competency models in collaboration with content experts. Develops and analyzes the Learning Needs Assessment for the department and clinical areas. Monitors compliance related to mandatory staff education
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the opportunity to work at the intersection of AI and health sciences, focusing on developing machine learning approaches to diagnosing musculoskeletal pain conditions, including low back pain and osteoarthritis
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Location Lexington, KY Grade Level 12 Salary Range $62,400-111,634/year Type of Position Staff Position Time Status Full-Time Required Education PhD Click here for more information about equivalencies: https
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from Hi-C and Capture Hi-C experiments. Have experience developing graphical user interfaces (GUIs). Candidates with knowledge or experience in machine learning methods will be prioritized. Successful
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Movement Sciences at Rutgers University is seeking a highly motivated Post Doctoral Associate to work on translational projects at the intersection of biomechanics, machine learning, exergaming, and mobile
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on Android phones including Kotlin and Java. This position requires skills in software development on Apple iOS phones including Swift. Experience in computational diabetes, signal processing, machine learning
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development in topics such as computer vision, audio signal processing, machine learning, deep learning, and/or sensor systems. Experience in collaboration and technology transfer to partners outside
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-fidelity finite element models to investigate surface wave propagation in soft biological tissues, forming the foundation for subsequent statistical and machine learning frameworks that integrate
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classification for conducting cutting-edge and life-changing research that creates impact in our communities. Additionally, for more than a decade, they have received a national Military Friendly® School
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to the development of novel indoor localization and tracking methods, algorithms, and systems ? Evaluating the performance of such methods, algorithms, and systems via modeling and simulation ? Performance evaluation