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University of Toronto Faculty of Information Sessional Lecturer Summer Term 2026 – Session Y (May – August) INF2179H – Machine Learning with Applications in Python Course Description: Machine
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that are contributed by BON in a Box collaborators. This includes understanding the analysis with support from publications, the team and the contributor, testing pipelines with different parameters, fixing R and python
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ruminant nutrition • Experience with quantitative or mechanistic modeling • Ability and willingness to work in Python-based environments Before applying, please note that to work
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empirical research. Applicants should be comfortable working with data and has some experience in either R/Stata/Python/Julia. The position requires strong organizational skills, care in handling data
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(Python, R), good communication skills, and basic knowledge of brain imaging. This position is covered by the AMURE collective agreement: https://www.mcgill.ca/hr/files/hr/2023
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& Reporting Stack Architect, develop, and maintain a bilingual (English/French) open-source decision-support dashboard using Shiny for Python/R, compliant with current accessibility standards (WCAG, keyboard
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skills Ability to work with nuclear industry data Good understanding of the nuclear sector in Canada Proficiency in at least one programming or statistical software package (Python, MATLAB, R, or Stata
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Fortran, Python and Matlab Statistical data analysis and visualization Communicating research results in written papers, reports and oral presentations Education: Bachelor’s degree, or equivalent training
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environments (e.g., MATLAB/Simulink, Python, C, C++) for control, guidance and navigation systems. Experience developing collision avoidance, path planning, sensor fusion, or situational awareness algorithms
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programming in Python, MATLAB, or C/C++; Experience with detection, classification, and geolocation of RF emitters, with demonstrated application to uncrewed aircraft systems (UAS), ISM-band devices, or similar