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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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in Shrivenham, will undertake high-quality research and support a range of professional military education courses at the postgraduate level. Successful applicants will teach and supervise students
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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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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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candidate who can lead teaching and practice around core modules such as software development, full stack web development, game development, cyber security, and machine learning. The successful candidate will
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to the subject of the course. To teach at the graduate level, faculty should hold an MS or PhD in computer science or a closely related field, or a Bachelor's degree in CS or a related field combined with
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military education courses at the postgraduate level. Successful applicants will teach and supervise students who are serving officers or civil servants in the UK and allied armed forces. There are also
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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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the end of the workshop. Through the workshop, students will learn to master the mechanics and conventions of academic writing, understand and adhere to submission guidelines and publication standards, and
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Computer Science or near completion. Higher language computer programming (python) General machine learning experience (vision, structured data, NLP) Machine learning libraries (SciKit Learn, pytorch,…) Scientific