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Field
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-compliance in ecological momentary assessment, or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models in small samples. The ideal candidate has prior
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machine learning frameworks. Proficiency in writing clean, efficient, and well-documented code. Mathematics skills including linear algebra and partial differential equations. Ability to implement software
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developing AI-based smart healthcare monitoring devices, focusing on biosensor development, lab-on-chip, and point-of-care technologies. Apply machine learning and computer vision techniques to analyze
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may include but are not limited to: algorithm and software development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning
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-scale assessment data, meta-analyses of meta-analyses) Methods and approaches to cumulative, living, and community-augmented meta-analyses Methods and approaches to include machine learning and artificial
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, sensor networks and measurement technology, grid computing and physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidate will be part of a research
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modeling. These projects will focus on how to leverage ideas from a combination of partial differential equation based computational methodologies and machine learning techniques to develop computational
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, or other related fields. Solid Mathematical skills. Experience in implementing algorithms for machine learning and natural language processing-related applications (It would be good if the candidate can also
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-edge research and development in EIT-based tactile sensing, machine learning for real-time state estimation, and sensory-motor control optimization for robotic systems. Funding is available for this post
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workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness