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Research Assistant 2 position is based in Prof. Amin Emad's research lab in the department of Electrical and Computer Engineering. The RA 2 will work in collaboration with the Research Supervisor to develop
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. This information will be considered to ensure the equitable assessment of the candidate’s record. McGill implements an employment equity program and encourages members of designated equity groups to self-identify
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equity program and encourages members of designated groups to self-identify. Persons with disabilities who anticipate needing accommodations for any part of the application process may contact, in
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knowledge to productively engage with diverse communities. McGill implements an employment equity program and encourages members of designated groups to self-identify. Persons with disabilities who anticipate
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orientations and gender identities, as well as from all qualified candidates with the skills and knowledge to productively engage with diverse communities. McGill implements an employment equity program and
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directly to the Principal Investigator to ensure priorities are addressed. Supervision Given None. Minimum Qualifications Completion of a relevant technical program or a university degree in a relevant
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for internal and external funding to support their research program; and undertake relevant administrative activities. Applicants must possess a DDS or DMD (or equivalent) degree and specialty training in
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Continuous Instructor(s), Electrical Apprenticeship and Electrical Installations Technology Programs
environment. Bring passion, enthusiasm, and ideas that will positively contribute to the advancement and growth of the program and the culture of our collaborative and dynamic team. Key Responsibilities
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(valid within 2 years) and CPR Level C (valid within one year). Responsibilities Plan, deliver and modify program lessons and activities. Ensure safety, well-being and enjoyment of campers from ages 4 - 15
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establishment of over 36 new faculty. Work Performed Develop, program, test, validate, and report on ML pipelines, including LLMs, and other machine learning models for heterogeneous health data extraction and