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assistant, and have expertise to teach foundational AI courses such as introduction to AI, machine learning, deep learning, and large language models, as well as advanced AI courses aligned with
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software development. E3 Experience in training deep learning models relevant in research projects at scale. E4 Experience of applying good software engineering practices including but not limited
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Government of Canada | Government of Canada Ottawa and Gatineau offices, Ontario | Canada | 25 days ago
to contribute to Foundations of Machine Learning research focus. Strategic Researchers at TIMC work on a variety of complex challenges and they draw on a variety of tools including deep mathematical theory and
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future. Fuelled by curiosity and a deep sense of responsibility, they provide invaluable contributions to research and teaching, thus enriching our society. Are you also inspired and driven by the desire
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. Demonstrated teaching excellence in higher education. Demonstrated ability to supervise students in learning projects. PREFERRED QUALIFICATIONS Earned a PhD from an AACSB-accredited institution. Experience in
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., turbine components).Research on advanced deep learning techniques, including architectures based on GRU, LSTM, attention mechanisms, and hybrid models.Implementation of real-time predictive models (soft
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25th February 2026 Languages English English English The Department of Materials Science and Engineering has a vacancy for a PhD Candidate in machine learning and large language models (LLMs
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fields of computer science, data science, artificial intelligence, machine learning, deep learning, computer vision, natural language processing, biocomputation, nerual networks, generative artificaial
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learning and statistics, and who are eager to contribute to impactful methods for generating private and fair synthetic data with good utility. This project involves development of deep learning based
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. They will learn to design interpretable, legally robust AI systems, including attention-based deep learning models and reinforcement learning approaches that adapt lineup presentation in real time based