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The expected pay for this position is $70,000 per year + benefits. AI and Deep Learning for Genomics, Transcriptomics, and Bioinformatics Job Summary The School of Biomedical Engineering at the University
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the Faculty of Applied Science & Engineering at the University of Toronto invites applications for a full-time teaching stream position in the area of Artificial Intelligence and Deep Learning. The appointment
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, sciences, and professional fields. The successful candidate will benefit from a robust, existing Inter-Faculty bridge between the Department of Equity, Ethics, and Policy (DEEP) and the Department
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this environment, professors, staff and students learn together and challenge one another in an open-spirited and inclusive community that values curiosity, engagement, and courage. McGill University seeks a Dean of
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Koziarski Lab - The Hospital for Sick Children | Central Toronto Roselawn, Ontario | Canada | 3 months ago
completed projects, preferably with publicly available repositories. Experience with deep learning frameworks such as PyTorch and JAX. Hands-on experience in developing and training deep learning models
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: Master’s degree in computer science, computer/software engineering, applied mathematics, artificial intelligence, or a related field. Strong skills in deep learning (e.g., PyTorch/TensorFlow). Experience in
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to develop deep learning models for analyzing whole-slide histopathology images, as well as natural language processing (NLP) methods for clinical records such as pathology reports and electronic health data
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Staff Scientist within SDL1: Inorganic Expertise in the following areas is desired: -Design, development, and operation of electrochemistry self-driving labs -Deep understanding of self-driving workflows
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emphasis on reflective learning has cultivated a deep interest in social justice, expressed through STM’s unique interdisciplinary programs (e.g., Catholic studies; critical perspectives on social justice
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: Optical Fibre Communications; David Plant ECSE 551: Machine Learning for Engineers; Mark Coats ECSE 552: Deep Learning; Course Lecturer ECSE 554: Applied Robotics; Hsiu-Chin Lin ECSE 597: Circuit Simulators