19 algorithm-sensor-"University-of-California" positions at University of Toronto in Canada
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Survey of the Universe’s Magnetism (POSSUM) using the Australian Square Kilometre Array Pathfinder (ASKAP), supervised by Prof Hložek and in collaboration with Prof Bryan Gaensler (University of California
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technical subjects such as programming, data science, machine learning, and algorithmic fairness is highly desirable. Candidates must have teaching experience in a degree-granting program, including lecture
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: Course number and title: MIE1624F/S – Introduction to Data Science and Analytics Course description: The objective of the course is to learn analytical models and overview quantitative algorithms
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 12 days ago
sensing concepts and utilizing remotely sensed data for monitoring land resources and environmental change. Topics include surface-energy interactions, sensor systems, image interpretation, and applications
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, Python, or C/C++, with the ability to develop custom scripts and algorithms for data analysis and modeling. Familiarity with rheological characterization techniques, such as rheometry or viscometry
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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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, strings, pointer-based data structures and searching and sorting algorithms. The laboratories reinforce the lecture topics and develops essential programming skills. Estimated course enrolment: ~150
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), students will explore the implications of choices in architecture across the range from mainframes and personal computing to mobile devices and sensors, understand the nature of different network and service
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Education recently ranked the University second among North American public institutions (after the University of California, Berkley) and 18th overall. The University of Toronto St. George campus is located
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, lists, maps. Program structure: control flow, functions, classes, objects, methods. Algorithms and problem solving. Searching, sorting, and complexity. Unit testing. Floating-point numbers and numerical