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with strong background in Computer Vision and Machine Learning. Position Overview: In this position, you will investigate the existing solutions for assessing datasets and developing a novel tool relying
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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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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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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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, policy, energy conversion, new business models, techno-economic and life cycle analyses, machine learning, optimization, AI, intelligent networks, among others. The PDF will join a project studying
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the following fields: Computer Architecture Algorithms and Optimization Health Research Human-Computer Interaction Machine Learning and ML Foundations Machine Perception Natural Language Processing (including
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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