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At the Technical Faculty of IT and Design, Department of Computer Science, one PhD stipend in quantum error correction codes and noise modelling is available within the general study programme
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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computational models, algorithms, or theory to probe the mechanisms of learning and memory. Ideal candidates will work across levels—from molecules and synapses to circuits and systems—and contribute
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decoding, the design of optimal codes, as well as related statistical inference problems. Successful candidates will have a genuine interest in Shannon Theory and will have a background in information theory
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psychological theories Knowledge of the application of educational and psychological theories to authentic settings Experience in college/university teaching in this subject Professional experience in teaching K
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to, coding (C# or C++), game engine proficiency (Unity/Unreal), gameplay/systems programming, games design, games theory, and an understanding of industry standard pipelines/workflow. It is essential you have
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 18 hours ago
: FAH310H5F Curating Matters: Contexts and Issues in Contemporary Curatorial Practice Topic description: This course will introduce students to the major critical texts, theories, and debates circulating in
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 18 hours ago
completion of any course grading not finished by December 31, 2025 for F courses. Course Code & Description Section/ Time Dates of Appointment Estimated Enrolment Estimated T.A. Hours Duties Qualifications
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activities are conducted in the English language, thereby requiring English communication both verbally and in writing. Hiring Unit: IGSF Course Title: Postcolonial Feminist Theories Subject Code: GSFS 304 001
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: Develop novel machine learning theories and techniques for analyzing noisy time-series data, with a particular focus on seismic signals Perform uncertainty quantification in time-series analysis to assess