743 computer-science-intern-"https:"-"https:"-"https:"-"https:"-"P" positions at McGill University
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(in both the Departments of Physics and in Computer and Electrical Engineering) and partners worldwide, leveraging industry-standard tools and technologies to ensure the quality and reliability
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for residents, fellows, Program Directors, and administrative staff, responding to inquiries related to policies, procedures, training requirements, and deadlines. Liaises with internal units and external
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conjunction with the Program Officer and the BME Operations team, perform tasks related to internal and external networking and communications; post to departmental social media channels, lead the assembly and
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. Attending regular Internal Organization Committee (IOC) meetings with representatives from student societies Preparing a short report due by the end of this contract focussing on key data points (Frosh
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preprocessing, cleaning, and reproducibility practices. Familiarity with national or international research computing infrastructures. Knowledge of responsible AI practices, including model evaluation, bias
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synthesis such as systematic reviews, scoping reviews Contribution to the implementation of internal laboratory projects related to organizational and technological innovations Complete IRB documents (e.g
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such as Computer Science, Software Engineering, and other related fields Knowledge of Python is considered an asset Solid command of the English language Knowledge of McGill campus and resources Ability
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at McGill University), do not apply through this Career Site. Login to your McGill Workday account and apply to this posting using the Find Jobs report (type Find Jobs in the search bar). International
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mitigation strategies; seek appropriate internal guidance and approvals; and implement agreed solutions. Work closely with the Technology Transfer Office to integrate appropriate intellectual property
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preprocessing, cleaning, and reproducibility practices. Familiarity with national or international research computing infrastructures. Knowledge of responsible AI practices, including model evaluation, bias