SL380489-Fall 2026-CIS*6530 Cyber Threat Intelligence & Adversarial Risk Analysis

Sessional Lecturer Work Assignment
Sessional Lecturer, Unit 2
Academic Unit: 
School of Computer Science
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: 
CIS*6530
Course Name: 
Cyber Threat Intelligence & Adversarial Risk Analysis
Course Format: 
In-Person
Course Description: 
See Course Calendar
Projected Class Enrolment: 
20-40
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: 
May be required to design hands-on labs, demonstrations, or applied exercises involving real or simulated cyber-attacks, threat intelligence feeds, security logs, SIEM/SOC workflows, or AI-assisted security analytics. This is important because the MCTI program emphasizes experiential, lab-based learning and use of the Security Operations Centre. Candidate will also be expected to prepare and deliver lectures, develop assignments and exams, provide timely feedback, hold office hours, manage CourseLink/D2L, coordinate with TAs, and submit grades on schedule.
Qualifications
Required Qualifications
Degree: 
PhD related to field
Other
PhD in Computer Science, Cybersecurity, Information Security, Artificial Intelligence, Data Science, Computer Engineering, or a closely related field.
Prior Teaching Experience: 
Successful teaching related to field at college or university level
Other
Post-secondary teaching experience in cybersecurity, information security, AI, data mining, or security operations.
Required competence, capability, skill and ability related to course content: 
Must have strong, current expertise in the following: • Cyber threat intelligence frameworks, intelligence lifecycle, collection, analysis, dissemination, and operationalization; • Advanced Persistent Threats, malware campaigns, threat actors, attack chains, and adversarial tactics, techniques, and procedures; • AI, machine learning, and data mining methods applied to cybersecurity, including anomaly detection, malware classification, alert triage, threat prediction, and security analytics; • SOC operations, SIEM, SOAR, incident detection and response, threat hunting, and security monitoring; • Adversarial risk analysis, cyber risk assessment, and intelligence-informed defensive strategy; and • Ethical, legal, and professional issues in collecting, labelling, storing, and sharing threat intelligence data. NOTE: A Master’s degree could be considered only in exceptional cases where the candidate has substantial senior industry experience in cyber threat intelligence, SOC leadership, threat hunting, or AI-driven security operations.
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.
Specific Preferred competence, capability, skill and ability related to course content: 
• Substantial industry or research experience in cyber threat intelligence, AI-driven security operations, malware analysis, incident response, SOC leadership, or threat hunting; • Experience with real-world security tools and frameworks such as MITRE ATT&CK, STIX/TAXII, MISP, OpenCTI, SIEM platforms, SOAR platforms, YARA, Sigma, Zeek, or related technologies; • Able to incorporate current threat actor case studies, hands-on exercises, and applied AI/security analytics into the course.
Days Required and Wages
Days and Times Required: 
Specific lecture times will be announced at a later date. Candidates should normally expect 3 hours of lecture time each week.
Period of the Work Agreement (Start Date and End Date): 
September 8, 2026 to December 24, 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): 
Wednesday, July 15, 2026
Posting Email Contact: 
socspost@uoguelph.ca
Hiring Contact Information: 
Lauren King, Instructional Support Coordinator - teachingsupport@socs.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.