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Field
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computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics
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on the intersection of data science, statistics and machine learning, all with applications to wind and solar energy science. You will be a part of an international team developing tools to plan and operate hybdrid
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Council of Canada). The research will focus on applying, developing, and implementing novel statistical methods for causal inference, integrative data analysis, and machine learning with large
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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(UQ) for machine learning and its validation. Your areas of research will be chosen based on both your own expert judgement and insight into trends and developments and on team requirements to ensure
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functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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expertise in data science, with hands-on experience in techniques such as machine learning, reinforcement learning, and simulations, and in handling large-scale, within-person, or real-time datasets; A strong
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computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics
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record (EHR) as well as MyChart data, with the opportunity to work on applications of machine learning/deep learning/ Natural Language Processing in novel areas of healthcare. The position is open for a