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: Course title: APS360H1 F – Applied Fundamentals of Deep Learning Course description: A basic introduction to the history, technology, programming and applications of the fast evolving field of deep
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qualifications, the need to acquire experience, previous experience, and previous satisfactory employment under the provisions of this collective agreement. Application Procedure: For detailed information
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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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McGill University | Winnipeg Sargent Park Daniel McIntyre Inkster SE, Manitoba | Canada | about 8 hours ago
. Qualifications: Masters degree in Computer Science. Proficient in Python programming, and deep learning packages like PyTorch and JAX. Prior research experience and publications. Before applying, please note that
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and managing scalable ETL (Extract, Transform, Load) pipelines to integrate multi-source, multimodal datasets Applying big-data analytic, including spatio-temporal modeling and deep learning techniques
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | 24 days ago
increasingly utilizes big data, satellite imagery, register data, and advanced methods such as deep learning and neural networks to address major societal challenges related to spatial inequalities and
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Sessional Lecturer - CTL7014H - Fundamentals of Teaching and Learning Course number and title: CTL7014H - Fundamentals of Teaching and Learning. Course description: This course will explore
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is a catalyst for transformative learning, insights and public engagement, bringing together diverse views and initiatives around a defining purpose: to create value for business and society. We make a
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. Both courses are connected to an inquiry-based, data driven school-specific Master of Teaching Junior/Intermediate cohort partnered with a TDSB school. As such, one instructor will be hired to teach
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libraries in Python (e.g., NumPy, Pandas, PyTorch, Seaborn, Scikit-learn) to visualize and model healthcare sensor data. Design and implement deep learning architectures for time-series data. Perform data