Application Deadline: We will review applications on a rolling-basis

For this exciting position, we are specifically targeting students who have a background in geology, geotechnical engineering, or geological engineering. Candidates must be enrolled at a Canadian post-secondary institution for the full duration of the co-op term and plan to return to their studies upon completion of the co-op term for at least one academic term. If you are not currently located near the work location, you must be willing to commute or temporarily relocate for the duration of the term. A valid study permit and studentship letter will be required where applicable.

This role focuses on supporting the implementation of machine-learning and computer-vision initiatives for geological and geotechnical drill-core logging and downhole imagery, including televiewer data. The successful candidate will work with structural geologists, geoscientists, and geotechnical engineers to test, validate, and implement automated logging tools, while supporting database management and workflow development.

You will gain hands-on experience working with complex datasets from major operations and advanced projects, gain insights into how automated interpretation tools are validated and deployed in practice, and how structural geology and geotechnical data are used in an exploration and mining context. You will also build experience connecting structural geology with digital innovation, while receiving mentorship from experienced structural geoscientists and technical experts in a collaborative Vancouver office environment!

Don’t miss out on this outstanding opportunity to be part of one of Canada’s leading mining companies and join our team!

Why consider Teck Resources Limited for your Co-op Placement?

Key Responsibilities

• Be a courageous safety leader, adhere to and sponsor safety and environmental rules and procedures
• Support machine learning and computer vision workflows for automated drill core logging (Rock Quality Designation and Fracture Frequency) and televiewer interpretation
• Prepare training and evaluation datasets for machine learning model development
• Test and validate automated drill core logging and televiewer results from machine-learning models and assess data quality issues and limitations of automated products
• Contribute to developing workflows and standard processes for integrating automated tools into project work
• Communicate findings, insights, and recommendations to the project team
• Support integration of structural data into 3D geological models
• Assist with additional structural or geotechnical tasks as needed

Qualifications

• Currently enrolled in a co-op program in Geology, Geological Engineering, or a related field
• Strong interest in structural geology, rock mass characterization, or geotechnical applications
• Curiosity about or exposure to machine learning, computer vision, or geoscience automation
• Familiarity with downhole imaging (optical or acoustic televiewer) is an asset
• Strong attention to detail with an interest in data validation and quality control
• Comfortable working with large datasets and technical software
• Ability to work independently and collaborate in a multidisciplinary team
• Strong written and verbal communication skills

Nice to have:

• Experience with drill core logging and televiewer interpretation
• Exposure to 3D geological modelling software
• Understanding of RQD, fracture frequency, or rock mass characterization schemes
• Curiosity about applied R&D and operational deployment of digital tools
• Previous experience in mining or geotechnical projects