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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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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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of this position may include: Single-cell RNA-seq, perturb-seq, and/or other transcriptomic analysis Next generation sequencing and bioinformatics analysis Machine learning/AI Analyzing data and presenting the data
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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