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University of British Columbia | Northern British Columbia Fort Nelson, British Columbia | Canada | 14 days ago
(Introduction to Software Engineering), CPSC_V 314 (Computer Graphics), CPSC_V 317 (Introduction to Computer Networking), CPSC_V 319 (Software Engineering Project), CPSC_V 320 (Intermediate Algorithm Design and
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), debuggers, code verifiers and unit test frameworks and gain experience in graphical user interface design and algorithm development. Posting end date: July 11, 2025 Number of positions (est): One (1) position
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. Key Responsibilities Lead AI/ML algorithm development for predicting plant water and nutrient uptake under varying environmental and growth conditions. Analyze multi-source data, including aerial and
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developing image analysis and machine learning algorithms and tools for aerial imaging and analysis. You will also contribute to data collection, data curation, and the development of a data portal for project
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efforts in the Algorithm and Harmonized Data Working Group and other Working Groups/Teams as necessary. Network and Research Administration Facilitates the operationalization of the Canadian Data Platform
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power of data science and algorithmic research with the fields of democratic theory, political science, and public policy. Ideally, the candidate has expertise and interest in innovative research using
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application of these techniques to the domain of information science. Topics will include software principles and practices, programming concepts and techniques, data structures, and algorithms. This course is
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there is a change to the date/time) Location: Chancellor Day Hall Course Description: Technology law of how to regulate AI algorithms. How technological innovation produces social change: human
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procedures (e.g., multilevel modeling, longitudinal data analysis, machine learning algorithms), cleaning and structuring large datasets, validating model assumptions, and ensuring reproducibility. Synthesizes
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond