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experience of the candidate At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and
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the collection, processing, and analysis of physiological data from recreational and elite athletes across various exercise protocols. Utilizing machine learning and deep learning algorithms, integrate multi-modal
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recovery. Oversee the collection, processing, and analysis of physiological data from recreational and elite athletes across various exercise protocols. Utilizing machine learning and deep learning
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transportation operations and network modelling, accessibility analysis, data analysis (statistics and/or machine learning methods), and spatial mapping. Because the work will involve multiple years of daily
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graduate students in disciplines relevant to chemical risk assessment (e.g., toxicology, chemistry, endocrinology, AI/machine learning) and governmental staff presently involved in chemical risk assessment
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data science, machine learning preferred. - Strong programming skills in R and/or Python required. We invite applications from qualified candidates who share our commitment to employment equity and
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and graduate students in disciplines relevant to chemical risk assessment (e.g., toxicology, chemistry, endocrinology, AI/machine learning) and governmental staff presently involved in chemical risk
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that change! Qualifications The position requires a PhD degree in electrical, computer or biomedical engineering, computer science, or a closely related area. The successful candidate is expected to develop
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contribute to the collaborative TQT research community. Principal Investigator: Na Young Kim Project Name: Solid-state analog Optimization Solver and Quantum Machine Learning (Theory) Research Area