for Teaching Assistants, Graduates Service Assistants & Sessional Lecturers

SL -Winter 2027-DATA*6400 Machine Learning for Sequential Data Processing

Sessional Lecturer, Unit 2

Academic Unit

Mathematics and Statistics

Semester(s) of Assignment(s)

Winter 2027

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 Number

DATA*6400

Course Name

Machine Learning for Sequential Data Processing

Course Format

In-Person

Course Description

See Course Calendar

35

Anticipated Duties and Responsibilities

- Orientation-Training
- Office Hours
- Preparation
- Student Consultation
- 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 timeseries data,



and the related preprocessing techniques - Must have knowledge/research/development experience related tothe modeling and application-level processing for sequential data such as classification, search, summarization, andtranslation - Must have solid experiences in machine learning techniques and their applications - Must be familiar withPython and related packages for machine learning and sequential data processing - Have experience in the applications ofdeep 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 sequentialdata
- 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 7, 2027 to May 7, 2027

Other Posting Information

Application Deadline (All postings will automatically expire at 11:59 pm on this day):

Friday, November 6, 2026

Posting Email Contact

Hiring Contact Information:

Rajesh Pereira, Chair, Department of Mathematics & Statistics, MacNaughton Rm 519, University of Guelph, Guelph, ON N1G2W1 Email:

Additional Information

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, protected 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: ).

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.

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📌 Machine Learning for Sequential Data Processing (Guelph)
🏢 University of Guelph
📍 Guelph