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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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transformations, geometric corrections, raster-vector conversions, attribute joins, and data cleaning using tools such as QGIS, ArcGIS, or R and Python geospatial libraries (e.g., R's sf and terra packages
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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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of Fortran, C,C++ under Linux/Unix, and a scripting language like R and Python. Demonstrated abilityto program efficiently on serial, vector and parallel systems, with the ability analyze and improve software
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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