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Field
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EPSRC PhD Studentship in: Big Data, Network Complexity and Machine Learning to Deliver Targeted Pro-active DWDS Maintenance
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Details Posted: 14-Jun-24 Location: Nashville, Tennessee Type: Full-time Salary: Open Categories: Academic/Faculty Internal Number: ADJUN002195 Teach undergraduate classroom- and/or lab courses in
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 15 hours ago
electronic health records and clinical text through machine (deep) learning modeling and natural language processing, the incumbent will also contribute to software package development, scientific paper
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captioning. Development of causal reasoning tools, including causal inference, counterfactual analysis, causal discovery. Development of deep learning methods and theory. Development of computer vision methods
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University of Toronto Faculty of Information Sessional Lecturer Fall Term 2024 (September - December) INF2404H – Explainability & Fairness for Responsible Machine Learning Course Description
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advances obtained on Graph Neural Networks, on both convolution [5] and pooling [6] steps. 1.2 METHODOLOGY AND WORK PLAN The proposed PhD research program aims to develop innovative graph machine learning
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networks. What You Will Do: Facilitate classroom lecture and discussions Engage students in learning course objectives and topics Assess student performance and mentor success in the classroom Provide a
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Sessional Lecturer, INF2205H - Designing Sustainable & Resilient Machine Learning Systems with MLOps
University of Toronto Faculty of Information Sessional Lecturer Winter Term 2025 (January - April) INF2205H – Designing Sustainable and Resilient Machine Learning Systems with MLOps Course
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University of Toronto Faculty of Information Sessional Lecturer Winter Term 2025 (January - April) INF2179H – Machine Learning with Applications in Python Course Description: Machine learning has
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: Possess a PhD degree in computer science, electronic engineering, applied mathematics, perception sciences, etc. Background knowledge in signal/data compression, and data-driven and machine learning