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Applicants who wish to have incumbency considered must declare it during the application process.

Course Information:

Course descriptions can be viewed on the Undergraduate and Graduate Studies Academic Calendars

Undergraduate studies - Academic Calendar

Graduate studies - Academic Calendar

Appointment Start Date:

2026-12-16

Appointment End Date:

2027-04-20

Number of Appointments:

Posting Information:

Key Responsibilities:

Course Development and Delivery:

Deliver engaging and informative lectures, seminars, and tutorials that align with the course outline and objectives.
Utilize various teaching methods and technologies to enhance student learning and participation, to comply with accessibility requirements, and to address the diversity of the material.
Design and develop course materials, if required, in alignment with the established curriculum, including syllabi, lecture notes, presentations, and reading lists.

Assessment and Evaluation:

Create, administer, and grade assignments, exams, projects, and other assessments.
Provide clear assessment criteria
Provide clear and constructive feedback to students on their performance.
Maintain student grades ensuring appropriate access and privacy.

Student Support:

Hold regular office hours to provide academic support and guidance to students.
Respond to student inquiries and concerns in a timely and professional manner.
Foster a supportive and inclusive classroom environment that encourages student engagement and collaboration.

Collaboration and Communication:

Work collaboratively with department faculty and staff to ensure course content and delivery meet academic standards.
Participate in meetings where appropriate and contribute to curriculum development and improvement.
Communicate effectively with students, colleagues, and university administration.

Administrative Duties:

Oversee the work assigned to graduate and undergraduate teaching assistants.
Complete administrative tasks related to course management, such as submitting grades by the Registrar’s Office deadline and prior to the end of the appointment, and maintaining course records to be provided to the hiring unit at the end of the appointment.
Adhere to university policies and procedures regarding academic integrity, grading, student conduct, accessibility and student information including Policies 33, 46, 70, and 71.
Coordinate course logistics, including scheduling of course support.
Complete training required for the position as specified by the University and/or the hiring unit.

Knowledge, Skills and Abilities

Communication Skills: Strong verbal and written communication skills.
Organizational Skills: Ability to manage course materials, assessments, and student records efficiently.
Student Engagement: Ability to engage and motivate students, fostering a positive learning environment.
Inclusivity: Ability to recognize and address the diverse needs of students, including cultural, linguistic, and learning differences, and to implement inclusive teaching practices
Professionalism: Adherence to university policies and procedures, including academic integrity and student conduct.

Education: PhD in a degree covered by Earth and Environmental Sciences (1), Health and Biomedical Sciences (1) and Physical Sciences (1) with research directly involving the use of AI/ML in those areas. Experience/Qualifications: Candidates should demonstrate expertise in the application of AI and ML to one of the following domain areas: Earth and Environmental Sciences, Health and Biomedical Sciences, and Physical Sciences. Relevant experience may include the use of AI/ML for analyzing complex scientific data; modelling environmental, biological, biomedical, or physical systems; pattern recognition; simulation and prediction; uncertainty quantification; image, signal, spatial, temporal, or multimodal data analysis; or other forms of computational discovery. Candidates should have in-depth knowledge of the foundations of AI and machine learning, including core concepts, methods, and workflows relevant to scientific discovery, exploration, modelling, prediction, and data-driven analysis. Practical experience or a research background involving AI-enabled scientific investigation is required. Candidates should be proficient with relevant tools, software, and methodologies, such as Python-based AI/ML frameworks, scientific computing environments, data preprocessing and visualization tools, model evaluation techniques, and responsible AI practices relevant to scientific applications. Candidates should be able to design and develop course materials aligned with the curriculum and learning objectives for Foundations of AI for Discovery and Exploration, including lectures, assignments, case studies, labs, and assessments that connect foundational AI concepts to real-world problems in Earth and Environmental Sciences, Health and Biomedical Sciences, and Physical Sciences. Experience collaborating with researchers, industry professionals, public-sector organizations, health or biomedical partners, environmental agencies, or scientific institutions using AI/ML for discovery, innovation, or applied research would be considered an asset. Department or School Name: Dean of Science Office Department Head Name: Chris Houser Campus: Waterloo main campus Delivery Mode: In person Course Credit Weight: 0.5 Audience: Undergraduate Wage Rate: $9688.93 Anticipated Enrolment #: 30 Enrolment Cap #: 50 TA Support: no Co-Teaching: no Special Topics Course: no New Course Development:yes Signficiant Course Development: no Course-Specific Information: Three sections: 001 - Applications of AI for the Physical Sciences - How can AI help us investigate questions in chemistry and physics? In this project-based course, students will choose a problem in the physical sciences and explore it throughout the term. As they learn new machine learning methods, they will test different approaches, compare their results, and examine what each method reveals about the underlying science. Lectures, quizzes, and hands-on work will provide the tools to build, evaluate, and communicate their findings; 002 - Applications of AI in Environmental Science and Ecology This course examines how artificial intelligence is being applied to address complex environmental and ecological challenges. Students will explore practical AI applications in areas such as remote sensing, species and habitat mapping, environmental monitoring, and ecosystem modelling. Through case studies and hands-on exercises using real environmental datasets, learners will gain experience applying AI tools to support research and identify opportunities for innovation. 003 - Applications of AI in Health and Biomedical Sciences This course examines how artificial intelligence is being applied to address complex challenges and support discovery in health and biomedical sciences. Students will gain hands-on experience applying AI tools and methods to analyze complex data, identify patterns, develop predictive models, support modelling and simulation, and generate new insights across biomedical, health, and medical contexts. Through case studies, practical exercises, and project-based learning, students will apply AI to tackle scientific questions and explore emerging applications in biomedical research and healthcare. Three hours of lecture/week plus one hour tutorial. The classroom must hold at least 200 people, as it is requested to be the same classroom for the lecture and tutorial. Tutorials will break-out in the large classroom. Suggested start date: December 15, 2026 Suggested end date: April 30, 2027

Equity Statement

The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg, and Haudenosaunee peoples. Our main campus is situated on the Haldimand Tract, the land granted to the Six Nations that includes six miles on each side of the Grand River. Our active work toward reconciliation takes place across our campuses through research, learning, teaching, and community building, and is coordinated within the Office of Indigenous Relations.

The University values the diverse and intersectional identities of its students, faculty, and staff. The University regards equity and diversity as an integral part of academic excellence and is committed to accessibility for all employees. The University of Waterloo seeks applicants who embrace our values of equity, anti-racism and inclusion. As such, we encourage applications from candidates who have been historically disadvantaged and marginalized, including applicants who identify as First Nations, Métis and/or Inuk (Inuit), Black, racialized, a person with a disability, women and/or 2SLGBTQ+.

Positions are open to qualified candidates who are legally entitled to work in Canada.

The University of Waterloo is committed to accessibility for persons with disabilities. If you have any application, interview, or workplace accommodation requests, please contact Human Resources at hrhelp@uwaterloo.ca or 519-888-4567, ext. 45935.