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University of Toronto Faculty of Information Sessional Lecturer Winter Term 2026 (January - April) INF2179H – Machine Learning with Applications in Python Course Description: Machine learning has
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. Strong coding skills in Python or a comparable programming language are expected, with the ability to develop analysis pipelines and tools that meet project deadlines and are suitable for publication
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selection-mass spectrometry or a similar technique, mass spectrometry for characterizing chemical mixtures or proteomic samples, and programming in Python or C. The ideal candidate should have experiences
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operations management, proficient in Python, and the ability to manage grades and course admin tasks on Quercus. Class Schedule: Courses may be online or in person depending on circumstances, so candidates
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using Python. Python and IBM Watson Analytics are modeling and visualization software used in this course. Practical aspects of computational models and case studies in Interactive Python are emphasized
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work independently and autonomously Proficiency in programming languages such as SQL, Python or R and datavisualization tools Assets (Nonessential): Experience working with startups, technology companies
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(cleaning, minor repairs) Experience in cheminformatics, including compound clustering, similarity searching, and molecular docking Proficiency and experience with data analysis in Python Experience working
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Python, Ruby, Javascript, and Unix/Linux shell Demonstrated experience and ability in building application software using web frameworks, and troubleshooting web applications Ability to communicate
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gained through experience or advanced training. Working knowledge of analytics tools and technologies such as SAS, R, Python, MS SQL Server, as well as visualization technologies such as, PowerB and/or
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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