SL193079-Fall 2021-ENGG*6500 Introduction to Machine Learning

Sessional Lecturer Work Assignment
Sessional Lecturer, Unit 2
Academic Unit: 
School of Engineering
Semester(s) of Assignment(s): 
Fall 2021
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: 
Yes
Number of Assignments that Carry the Right of First Refusal: 
1
Course Details
Course Number: 
ENGG*6500
Course Name: 
Introduction to Machine Learning
Course Format: 
In-Class
Course Description: 
See Course Calendar
Other Course Description or Assignment Information: 
Should conditions change and requirements are placed on the University and its employees by public health bodies, local, provincial, and federal governments, courses posted as ‘In-Class’ may have to switch to an alternative delivery format (see https://opened.uoguelph.ca/academic-continuity). By applying to this work assignment, you are agreeing to fulfill the duties in the mode of delivery posted and switch as required. The University’s COVID-19 website provides important information about campus protocols and guidelines for those working on campus.
Projected Class Enrolment: 
50
Anticipated Duties and Responsibilities
Anticipated Duties and Responsibilities: 
Orientation-Training
Office Hours
Preparation
Student Consultation
Lecturing
Email Correspondence/Monitoring
Conducting Labs/Seminars
Invigilating Exams
Grading
Qualifications
Required Qualifications
Degree: 
Masters related to field
Prior Teaching Experience: 
No teaching experience required
Required competence, capability, skill and ability related to course content: 
The successful candidate will have a minimum of a Master’s degree in computer engineering, computer science, or related computational discipline. The successful candidate should also have demonstrated experience applying machine learning in practice, either in research, employment, or contributions to open source projects. Successful completion of ENGG*6500 or equivalent course during an engineering undergraduate or graduate degree, or demonstrated teaching experience in an equivalent course.
Preferred Qualifications
Degree: 
PhD related to field
Other
PhD in Robotics or related field
Prior Teaching Experience: 
Successful teaching related to field at college or university level.
Specific Preferred competence, capability, skill and ability related to course content: 
The successful candidate will preferably have a Ph.D degree in computer engineering, computer science, or related computational discipline. Ideal candidates will have a strong publication record in Machine Learning conferences and journals. Experience with scientific computing with Python and modern machine learning / deep learning frameworks, e.g. PyTorch and TensorFlow is an asset.
Days Required and Wages
Days and Times Required: 
LEC: F 8:30AM - 11:20AM 9/9/2021 - 12/17/2021
Period of the Work Agreement (Start Date and End Date): 
September 7, 2021 to December 24, 2021
Wages (per semester, per full-load): 
minimum $7,617.56 (effective 2021/22)
Other Posting Information
Application Deadline (All postings will automatically expire at 11:59 pm on this day): 
Tuesday, August 10, 2021
Posting Email Contact: 
soe3913@uoguelph.ca
Hiring Contact Information: 
Bahram Gharabaghi, Associate Director of Graduate Studies, bgharaba@uoguelph.ca, THRN 2417, School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1 519-824-4120 ext. 58451

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)