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: Machine learning/deep learning model development for biomolecular data analyses and prediction Research Area: Data science and computational chemistry Required Skills: A Ph.D. in relevant field within
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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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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
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learning, offers powerful solutions to automate these tasks and provide reliable real-time information. This doctoral project is part of a 5-year research chair on Computer vision applied to the swine sector
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-based prediction. This includes instrumenting a high-power APS torch with video and acoustic sensors, developing machine learning algorithms for feature extraction, and building predictive models
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Learning Course Description: Machine Learning applications are increasingly utilized to make crucial decisions in many sectors of our economy and society. These include, but are not limited to, healthcare
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plays a pivotal role in shaping the digital learning environment at SAIT. This position is responsible for designing, implementing, and optimizing technology solutions that support teaching, learning, and
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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
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 2 days ago
to enhance students' learning experiences and sense of belonging. Excellent computer and technical skills (e.g. Access, Word, Excel, PowerPoint, Outlook). Superb interpersonal, listening, and communication
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application of natural language processing (NLP) and other machine learning methods, including training and deploying large language models (LLMs) for extracting data from electronic health records (EHRs