TA (CUPE) - DTI6302 D - Winter 2027

Posting Reason:

New Position

Location where work is to be performed:

Main Campus

Session:

2027 Winter Semester | Trimestre d'hiver

Faculty:

Faculté de génie / Faculty of Engineering

Unit:

School of Engineering Design and Teaching Innovation_ST

Job Classification:

Teaching Assistant/Demonstrator/Lab Monitor (CUPE)

Posting Type:

Cours précis / Specific course

Course Title:

Topics in Applied Data Science

Course Code:

DTI6302

Section:

D

Supervisor name (if known):

Mahtab Haj Ali

Date Posted (YYYY/MM/DD):

2026/10/27

Applications must be received BEFORE (YYYY/MM/DD):

2026/11/12

Description 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 :12Correction - Notation / Grading :15Surveillance / Proctoring :5Formation / Training :0Autre / Other :0

Number of positions:

2

Expected Enrolment:

30

Work Start Date:

janvier 04, 2027

Work End Date:

avril 30, 2027

Total Work Hours:

32.5

Hourly Rate:

AE-RA-Tuteur / TA-RA-Tutor : $33.83 ou/or $53.31 (Sept. 2026 - Aug. 2027)

Undergraduate students will be paid at the lower rate; while graduate students will be paid at the higher rate.

These rates do not include vacation pay nor statutory pay.

Method of Delivery:

Proctoring Mi-term and Final Exam

Language of Work:

Anglais | English

Requirements and Nature of Work:

"Preference will be given to candidates currently enrolled in a thesis-based graduate program in Digital Transformation and Innovation, Computer Science, or a related field, with a thesis topic focused on machine learning, MLOps, or their applications. A minimum GPA of 8.0 is required to ensure a strong academic foundation and understanding of the subject matter.

Candidates must possess a robust knowledge of Python and machine learning development environments. They should be familiar with open-source machine learning libraries, including TensorFlow, PyTorch, and Scikit-learn. Proficiency in Git for version control is also required.

Preference will be given to candidates who have used machine learning in published work, especially if it demonstrates the application of open-source libraries and APIs. Practical experience in software engineering and programming is required."

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.