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Field
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the following: To independently undertake research in machine learning. To publish high-quality research papers as required by the funding body or for dissemination to the wider academic community. To provide
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a new computational paradigm that combines the versatility of the digital computer with the efficiency of close-to-physics computing. The group targets the full computational stack, from materials
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bring direct experience or novel approaches to incorporating generative Artificial Intelligence (AI) (and related tools) to enhance student learning. Public syllabi for courses offered in our program can
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-time, grant-funded, annually renewable position with an appointment ending August 30, 2028. The position is primarily computer- and meeting-based and involves extended computer use and participation in
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learning and data analysis. Familiarity with one or more of the following areas: artificial intelligence, natural language processing, computer vision, multimodal learning, affective computing, human-robot
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industry-connected learning through placements, FUSECo projects, Industry Honours and Masters projects, Enterprise PhDs and other industry opportunities. Lead and grow industry engagement capability by
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architecture of entirely new foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
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and student guidance. Assist in the preparation and writing of research grant proposals. Job Requirements: PhD degree in computer engineering, Computer Science, Electronics Engineering or equivalent
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-trivial software. Solid understanding of algorithms, numerical methods, scientific computing, or machine learning. Ability and motivation to write clean, maintainable and well-tested code. Strong interest