SL396038-Fall 2026-ENGG*6090*03 Autonomous Systems and Robotics

Sessional Lecturer Work Assignment
Sessional Lecturer, Unit 2
Academic Unit: 
School of Engineering
Semester(s) of Assignment(s): 
Fall 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: 
ENGG*6090*03
Course Name: 
Autonomous Systems and Robotics
Course Format: 
In-Person
Course Description: 
See Course Calendar
Other Course Description or Assignment Information: 
The University of Guelph has vaccination and masking policies that may be altered with limited notice depending on COVID-19 circumstances and advice from government, public health authorities, and prevailing science. Currently, the vaccination and masking policies are paused. With the evolving nature of the pandemic, public health measures may resume with little advance notice to address the health risks. Should this occur, vaccinations will be required. You can choose to submit proof of vaccination before your start date. The University of Guelph has a COVID-19 Vaccination Policy and provides more Information about COVID-19 for Faculty and Staff.
Projected Class Enrolment: 
60
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
Other Duties Described: 
Prepare and deliver all scheduled lectures for the assigned section(s) in accordance with the approved course outline and the Calendar description. All laboratories and project demonstrations are completed in a physics-based simulator using an industry-standard toolchain (ROS 2, Gazebo, RViz, Navigation2). Assessment includes quizzes, a midterm examination, eight simulation laboratories, an integrated autonomy team project, and a cumulative final examination. Prepare, administer, and grade all assessments, including quizzes, the midterm examination, the final examination, and all components of the integrated autonomy project, and submit final grades by the Registrar's deadline. Coordinate and supervise the weekly simulator-based laboratory, including preparation and maintenance of the supported software image, starter packages, maps, datasets, and automated evaluation scripts, and oversight of Graduate Teaching Assistants across laboratory sections. Supervise the integrated autonomy team project through its proposal, design review, simulation demonstration, and final report and code submission, and assess individual technical accountability through code review, demonstration questioning, or contribution records. Enforce the reproducibility requirements of the course, verifying that submitted results can be regenerated from the submitted source code, configuration files, map files, random seeds, and launch instructions. Enforce course policy that simulation results are identified as simulation results and are not represented as physical-robot experimental results, and that course software is not connected to physical robots, vehicles, or external actuators without written authorization. Hold regularly scheduled office hours and respond to student inquiries in a timely manner. Maintain the CourseLink (D2L) course site, including posting of materials, gradebook management, and announcements. Implement academic accommodations arranged through Student Accessibility Services and apply University policy on academic consideration and academic misconduct. Provide assessment data and samples of student work required for program-level outcome reporting, including CEAB graduate attribute reporting where applicable. Submit the course outline for departmental approval prior to the start of the semester and attend course coordination meetings called by the Chair or course coordinator.
Qualifications
Required Qualifications
Degree: 
PhD related to field
Prior Teaching Experience: 
Other
Prior university-level teaching experience completed within the last five (5) years.
Required competence, capability, skill and ability related to course content: 
Degree: Completed Ph.D. in Electrical Engineering, Computer Engineering, Robotics, Systems and Control, Computer Science, or a closely related discipline, from a recognized university. Applicants whose Ph.D. is not complete as of the application deadline will not be considered. Required Expertise and Experience: Demonstrated command of the full course content: autonomous-system architectures and autonomy levels; differential-drive kinematics, non-holonomic constraints, and odometry; feedback control for mobile robots including waypoint control and trajectory tracking; sensor modelling for encoders, inertial units, LiDAR, and RGB and depth cameras; Bayesian estimation, Kalman and extended Kalman filtering, and sensor fusion; localization including scan matching and particle filters; occupancy-grid mapping, SLAM, and loop closure; global path planning including Dijkstra, A*, cost maps, and sampling-based methods; local motion planning, dynamic obstacle avoidance, and recovery behaviors; mission-level autonomy using finite-state machines and behavior trees; and multi-robot autonomy, safety assurance, and cybersecurity. Applicants must map their prior teaching or professional experience to this topic list in the cover letter. Demonstrated working proficiency with ROS 2, including nodes, topics, services, actions, parameters, launch files, coordinate-frame management, logging, and data recording. Applicants must name the ROS distributions they have used and the instructional or professional context in which they used them. Demonstrated ability to build and evaluate autonomy stacks in a physics-based simulation environment using Gazebo, RViz, Navigation2, and SLAM software. Applicants must name the specific packages and simulators used and describe at least one complete sensing, estimation, planning, and navigation stack they have implemented or supervised. Demonstrated ability to develop, debug, and document robotics software in Python and C++ on Linux, using Git for version control, and to construct experiments that are reproducible from source code, configuration, maps, and random seeds. Demonstrated ability to design, supervise, and assess a computer-based engineering laboratory, including preparation of laboratory specifications, starter packages, datasets, and marking schemes. Applicants must document at least one semester of responsibility for a computing, robotics, or software-intensive engineering laboratory and name the tools used. Demonstrated ability to supervise team design projects with individual technical accountability, including code review, demonstration questioning, and evaluation of contribution records. Applicants must state the number of project teams they have supervised and the assessment methods they used. Working knowledge of outcomes-based assessment and CEAB graduate attribute data collection. Applicants must describe a specific instance in which they collected, analysed, or reported graduate attribute or program-level outcome data. Proficiency with CourseLink (D2L) or an equivalent learning management system. Applicants must name the system and the functions they have administered. Excellent spoken and written English, with demonstrated ability to communicate quantitative and software-intensive material clearly to senior undergraduate and graduate students. Availability to teach in person on the Guelph campus at the scheduled lecture and laboratory times. Applicants must confirm this availability explicitly in the cover letter. Prior Teaching Experience: Required. Prior university-level teaching experience in robotics, autonomous systems, control, state estimation, computer vision, or a closely related quantitative and software-intensive engineering subject, completed within the last five (5) years. This requirement is met by either of the following: (a) at least one full semester as instructor of record for such a course; or (b) at least one full semester as a Graduate Teaching Assistant in such a course, including laboratory, tutorial, or seminar instruction and assessment. Applicants must document the experience in the application, naming the course, the institution, the semester, their role, and the supervising instructor.
Preferred Qualifications
Degree: 
PhD related to field
Licensed Professional Engineer (P.Eng.) in Ontario, or eligibility for licensure with Professional Engineers Ontario.
Prior Teaching Experience: 
Other
Experience teaching at the graduate level and supervising graduate student projects.
Specific Preferred competence, capability, skill and ability related to course content: 
Degree: Earned Ph.D. with a research focus in robotics, autonomous systems, probabilistic estimation, motion planning, or cyber-physical systems. Other: Industrial or research experience in mobile robotics, autonomous vehicles, unmanned aerial systems, warehouse or agricultural automation, or robotic inspection. Documented contributions to open-source robotics software, including ROS 2 packages, simulation models, or navigation and SLAM tooling. Experience deploying ROS 2 software on physical robotic platforms and migrating designs from simulation to hardware. Experience teaching within a CEAB-accredited engineering program. Prior Teaching Experience: One or more semesters as instructor of record for ENGG*6090 or an equivalent robotics or autonomous-systems course, with documented evidence of effective teaching.
Days Required and Wages
Days and Times Required: 
Course Format: Lecture: 3 hours per week, delivered as two 80-minute sessions. Laboratory: 2 hours per week, simulator-based and delivered in a computer laboratory. In-person delivery, Guelph campus. Lectures: Tuesday and Thursday (11:30 a.m.-12:50 p.m.). Laboratory: 2 hours per week, scheduled by the Department.
Period of the Work Agreement (Start Date and End Date): 
September 8, 2026 to January 7, 2027
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): 
Friday, September 4, 2026
Posting Email Contact: 
soe3913@uoguelph.ca
Hiring Contact Information: 
Posting Email Contact: soe3913@uoguelph.ca Hiring Contact Information: ecchair@uoguelph.ca Required Application Materials: A complete application consists of all of the items listed below. An application that omits any item, or that does not address each required qualification, will be assessed as not meeting the minimum requirements of this work assignment and will not be scored. A cover letter of no more than two (2) pages that addresses each required qualification listed above, in the order listed, with specific supporting evidence for each. A current curriculum vitae that includes a teaching history table listing, for every course taught: course code and title, institution, semester and year, role (instructor of record or teaching assistant), enrolment, and number of sections. Evidence of teaching effectiveness for the two most recent courses taught, whether as instructor of record or as a Graduate Teaching Assistant: student feedback questionnaire summaries, teaching evaluations, a peer teaching review, or a written assessment from the supervising instructor. An applicant who cannot supply this evidence must state the reason in the cover letter. One course syllabus authored by the applicant, and one laboratory specification, project brief, or assignment authored by the applicant with its solution and marking scheme. Proof of the completed Ph.D. (transcript or degree certificate), or written confirmation that it will be provided on request. Names and contact information for two (2) referees who can speak directly to the applicant's teaching. Applicants may also provide a link to a publicly accessible code repository containing robotics software they have authored, which will be considered as supporting evidence of the required technical qualifications.

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.