CUPE-Automne/Fall 2025- Corrector-ELG5255F
SITE, Canada
University of Ottawa
Location where work is to be performed:
Main CampusSession:
2025 Fall Semester | Trimestre d'automneFaculty:
Faculté de génie / Faculty of EngineeringUnit:
EECS_STJob Classification:
Corrector (CUPE)Posting Type:
Cours précis / Specific courseCourse Title:
Applied Machine LearningCourse Code:
ELG5255FSection:
FSupervisor name (if known):
Date Posted:
July 11, 2025Applications must be received BEFORE:
July 25, 2025Description of tasks (hours):
Unless specified below, specific allocation of hours to various tasks should be articulated in a description of work negotiated and signed by you and the supervising professor prior to the commencement of work.
Recherche / Research:0Préparation / Preparation:0Contact avec étudiants / Contact with Students:0Correction - Notation / Grading:0Surveillance / Proctoring:0Formation / Training:0Autre / Other:0Number of positions:
1Expected Enrolment:
20Work Start Date:
September 01, 2025Work End Date:
December 31, 2025Total Work Hours:
65Language of Work:
Anglais | EnglishGraduate Hourly Rate:
36.63Undergraduate Hourly Rate:
30.53Requirements and Nature of Work:
Nature of Work:
Mark, grade, calculate and record grades of students' work.
Minimum Requirements:
Must have completed a machine learning (or its equivalent) and data mining course with a mark of A or better. Must have passed Probability and Statistics (or an equivalent course) in their undergrad with a mark of (or equivalent to) A or better. Experience with both of the following environments: Matlab Statistics and Machine Learning Toolbox and TensorFlow. Attend a TA workshop if necessary. Must be ready for an interview if considered for this position.
Please note that the teaching method will be delivered: in person.
NOTE: le nombre de postes et le nombre d'heures offertes sont tous deux assujettis aux inscriptions finales dans ce cours. Please note that the number of hours offered may varie due to enrolments in that course.
Additional Information and/or Comments:
All University of Ottawa employees are required under provincial law to successfully complete all mandatory legislated training offered by the University. The list of training requirements may be modified by provincial law. If you are invited to continue the selection process, please notify us of any particular adaptive measures you might require. We may consult with the Health and Wellness sector of Human Resources, if needed. Any information you send us will be handled respectfully and in complete confidence.
The hiring process will be governed by the current CUPE 2626 collective agreement; you can click here to find out more.
The University of Ottawa embraces diversity and inclusion in the workplace. We are passionate about our people and committed to employment equity. We foster a culture of respect, teamwork and inclusion, where collaboration, innovation, and creativity fuel our quest for research and teaching excellence. While all qualified persons are invited to apply, we welcome applications from qualified Indigenous persons, racialized persons, persons with disabilities, women and LGBTQIA2S+ persons. The University is committed to creating and maintaining an accessible, barrier-free work environment. The University is also committed to working with applicants with disabilities requesting accommodation during the recruitment, assessment and selection processes. Applicants with disabilities may contact the academic unit to communicate the accommodation need.
Prior to May 1, 2022, the University required all students, faculty, staff, and visitors (including contractors) to be fully vaccinated against Covid-19 as defined in Policy 129 – Covid-19 Vaccination. This policy was suspended effective May 1, 2022 but may be reinstated at any point in the future depending on public health guidelines and the recommendations of experts.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Classification Data Mining Engineering Machine Learning Matlab Research Statistics Teaching TensorFlow
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