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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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Course Code & Title: BCH2203 Introduction to Programming in Python COURSE DESCRIPTION: This course is to introduce graduate students to the programing language Python. The course will teach
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conform to the expected result of the indicator. Creates proof of concept scientific workflows and documentation examples. Provides expertise in R and python coding to facilitate data analysis and workflow
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artificial intelligence in health research. Responsibilities Assist with data preprocessing and analysis of large neuroimaging datasets Support development and evaluation of machine learning models in Python
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). The Bioinformatics Analyst position corresponds to junior members of the team with expertise in large-scale genomic analyses, software development using R and Python, and High-Performance and Cloud computing
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optimization, and be proficient in Python and modern ML/RL libraries. Experience with robotic control systems, laboratory automation, simulation platforms, or interfacing software with hardware is highly
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, or probabilistic modeling, and be proficient in Python and modern machine-learning frameworks (ideally PyTorch). Experience with single-cell transcriptomics, epigenomics, proteomics, spatial omics, or multimodal
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possess significant expertise in programming with Python and PyTorch and using high performance computing environments. In particular, we are looking for experience in developing machine learning models
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scripts to process and analyze behavioural and neuroimaging data in languages such as Python, Matlab, or Bash and/or using standard neuroimaging analysis software; Conducts statistical analyses using tools
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Qualifications Experience with hyperspectral data acquisition and analysis, including Python-based workflows. Familiarity with machine learning, data integration, and advanced visualization tools for exploration