SL358412-Winter 2026-DATA*6400*01 Machine Learning for Sequential Data Processing

Sessional Lecturer Work Assignment
Sessional Lecturer, Unit 2
Academic Unit: 
Mathematics and Statistics
Semester(s) of Assignment(s): 
Winter 2026
Number of Available Work Assignment(s) / Sections: 
1
Level of Work Assignment(s): 
1
Right of First Refusal (RoFR)
A Sessional Lecturer holds a RoFR (i.e., for a particular course) if they have successfully taught the course in the past six (6) semesters. A SL who holds a RoFR to this course is required to exercise their right by way of the online hiring system. Also see: What is Right of First Refusal (RoFR)?
A Sessional Lecturer Currently Holds a Right of First Refusal for this Course: 
No
Course Details
Course Number: 
DATA*6400*01
Course Name: 
Machine Learning for Sequential Data Processing
Course Format: 
In-Person
Course Description: 
See Course Calendar
Projected Class Enrolment: 
35
Anticipated Duties and Responsibilities
Anticipated Duties and Responsibilities: 
Orientation-Training
Office Hours
Preparation
Student Consultation
Lecturing
Email Correspondence/Monitoring
Conducting Labs/Seminars
TA Coordination Meetings
Invigilating Exams
Grading
Qualifications
Required Qualifications
Degree: 
Masters related to field
Prior Teaching Experience: 
Successful teaching related to field at college or university level
Other
Required competence, capability, skill and ability related to course content: 
Must understand the major characteristics of sequential data such as natural language text, biological sequences, and time series data, and the related preprocessing techniques - Must have knowledge/research/development experience related to the modeling and application-level processing for sequential data such as classification, search, summarization, and translation - Must have solid experiences in machine learning techniques and their applications - Must be familiar with Python and related packages for machine learning and sequential data processing - Have experience in the applications of deep learning models, especially the large pre-trained models such as Bert and GP
Preferred Qualifications
Degree: 
PhD and expert in course content
Prior Teaching Experience: 
Successful teaching related to field at college or university level.
Many years of successful teaching related to contents of the course.
Other
Successful teaching experience at the graduate level
Research Experience: 
Quality and or Recent Research activity in areas relevant to the course demonstrating knowledge of current developments in course content.
Specific Preferred competence, capability, skill and ability related to course content: 
- Significant, recent teaching or industry experience related to the processing techniques for sequential data - Experience with managing groups, case studies, and flipped classrooms.
Days Required and Wages
Days and Times Required: 
Lectures: Tuesdays and Thursdays 11:30AM-12:50PM (see WebAdvisor for official listing)
Period of the Work Agreement (Start Date and End Date): 
January 2, 2026 to April 28, 2026
Wages (per semester, per full-load): 
minimum $8,838.51 (effective 2025/26)
Other Posting Information
Application Deadline (All postings will automatically expire at 11:59 pm on this day): 
Thursday, November 6, 2025
Posting Email Contact: 
mspost@uoguelph.ca
Hiring Contact Information: 
Rajesh Pereira, Chair, Department of Mathematics & Statistics, MacNaughton Rm 519, University of Guelph, Guelph, ON N1G2W1 Email: pereirar@uoguelph.ca

At the University of Guelph, fostering a culture of inclusion is an institutional imperative. The University invites and encourages applications from all qualified individuals, including from groups that are traditionally underrepresented in employment, who may contribute to further diversification of our Institution. For more information, the Office of Diversity and Human Rights (DHR) is a welcoming, safe and confidential one-stop shop for information, training and support on issues relating to diversity and human rights on our campus.
SL work assignments are unionized with CUPE3913 and their terms and conditions of work are covered by the Unit 2 Collective Agreement between the University and CUPE 3913 (email contact: president@cupe3913.on.ca).

All applicants must be eligible to work in Canada specifically at the University of Guelph before applying for an academic work assignment. All successful applicants must perform their work in Ontario and must be able to attend on-campus in-person meetings as required.