Research Intern – AI-Numerical Modelling for Sustainable Mining, School of Engineering and Computer Science

Laurentian University; Northeastern Ontario; Hybrid; CAD 36,000 - 44,000; Full time

Job summary

Laurentian University in Ontario, Canada invites applications for a Research Intern – AI-Numerical Modelling for Sustainable Mining in the School of Engineering and Computer Science. The position is grant-funded with a variable schedule and a 35-hour work week, ending December 31, 2027.

The intern will develop and calibrate numerical models using ITASCA/PFC, run parametric simulations, and apply AI/ML techniques as needed.

Qualifications

• Master's degree in Mining Engineering, Rock Mechanics, Geotechnical Engineering, Geological Engineering, Civil Engineering, Structural Engineering, or closely related field with numerical modelling experience.
• Demonstrated experience in numerical modelling of mining, rock mechanics, geomechanics or related problems is mandatory.
• Experience with numerical modelling software such as ITASCA/PFC or FLAC is required; other ITASCA tools are assets.

Responsibilities

• Develop and calibrate numerical models of tire-road interaction under underground mining conditions using ITASCA/PFC or similar software.
• Conduct numerical simulations for a limited, representative set of mining conditions.
• Analyze results to identify parameters affecting wear and particle generation.
• Organize results into structured datasets and apply AI/ML techniques as appropriate (≤20%).
• Document methodology and prepare technical reports and publications.

Skills

Numerical modelling

Data analysis

Programming basics

AI/ML exposure

Technical writing

Independent work

Team collaboration

Education

Master's degree (Mining Eng / Geotechnical / Rock Mechanics or related)

Tools

ITASCA PFC

Particle Flow Code (PFC)

FLAC

Fast Lagrangian Analysis of Continua

Job description

Research Intern – AI-Numerical Modelling for Sustainable Mining, School of Engineering and Computer Science

Position No.: JOBPOST-2026-001365

Salary: $39,960 per year

Competition ends: Tuesday, October 27th at 4:30 pm

Grant appointment until December 31, 2027This position is designated as one that requires a variable work schedule. However, it is understood that your work week will consist of 35 hours.

Responsibilities

This opportunity is proudly supported by Northern Ontario Heritage Fund Corporation and is funded through the Workforce Development Program. Eligibility requirements of the program can be found here: Workforce Development Program - Home | NOHFC

The Research Intern will contribute to an applied research project focused primarily on numerical modelling of tire-road interaction and tire-induced microplastic generation under underground mining conditions. Approximately 80% of the research will involve numerical modelling using appropriate numerical modelling software, such as the ITASCA software suite, particularly Particle Flow Code (PFC). The study will focus on a limited and logically selected set of representative mining conditions that can reasonably be investigated within the oneyear internship. The intern will develop and calibrate numerical models, conduct selected parametric simulations, and analyze the resulting data. Artificial intelligence and machine-learning methods may be used as a supporting component and are expected to represent no more than approximately 20% of the research work.

• Develop and calibrate numerical models of tire-road interaction, tire wear, and particle detachment under underground mining conditions using appropriate numerical modelling software, such as the ITASCA software suite, particularly Particle Flow Code (PFC).
• Conduct numerical simulations for a limited and logically selected set of representative mining conditions and parameters appropriate to the one-year project duration.
• Analyze simulation results and identify the main parameters affecting tire wear and tire-induced particle generation.
• Organize selected simulation results into a structured dataset and, where appropriate, apply limited artificial intelligence or machine-learning methods to support prediction and interpretation. AI-related activities will represent no more than approximately 20% of the overall research work.
• Document the numerical modelling methodology and results and contribute to technical reports, presentations, and research publications.

Qualifications

• Master's degree in Mining Engineering, Rock Mechanics, Geotechnical Engineering, Geological Engineering, Civil Engineering, Structural Engineering, or a closely related field with relevant numerical modelling experience.
• Demonstrated experience in numerical modelling of mining, rock mechanics, geomechanics, geotechnical, or closely related engineering problems is mandatory.
• Experience with numerical modelling software applicable to mining, rock mechanics, geomechanics, or related engineering problems is required. Experience with the ITASCA software suite, particularly Particle Flow Code (PFC), is considered an asset. Applicants from other disciplines must also demonstrate relevant numerical modelling experience in rock mechanics, geomechanics, or related engineering applications.
• A minimum of one(1) year of relevant research or technical experience involving numerical modelling of mining, rock mechanics, geomechanics, geotechnical, or closely related engineering problems.
• Experience obtained through a master's thesis, graduate research, research assistantship, or relevant professional work may be considered. Experience with Particle Flow Code (PFC), Fast Lagrangian Analysis of Continua (FLAC), or other ITASCA numerical modelling software is considered an asset.
• Strong knowledge of numerical modelling and computational methods applied to mining, rock mechanics, or geomechanics.
• Experience with Particle Flow Code (PFC), Fast Lagrangian Analysis of Continua (FLAC), or other ITASCA numerical modelling software is considered an asset.
• Understanding of model development, calibration, validation, and parametric analysis.
• Knowledge of underground mining and/or rock mechanics and geotechnical engineering.
• Basic programming, data analysis, artificial intelligence, or machine-learning experience is considered an asset but is not a substitute for the required numerical modelling background.
• Strong analytical, technical writing, documentation, and scientific communication skills.
• Ability to work independently and collaboratively within a research environment.
• Ability to work fluently (verbal and written) in both official languages, French and English, is an asset.

Applications are being accepted to fill an active vacancy within the University.

We are aware that some applicants are experiencing difficulty using our careers portal. Should you complete an application through our online form and receive an error, please submit your application to [e-mail]

The official University hours of operation shall be from 9:00 a.m. to 4:30 p.m., Monday through Friday, during, and including, the months from September to April, and from 8:30 a.m. to 4:00 p.m Monday through Friday during and including May to August., amounting to thirty-three and three-quarter (33.75) hours per week.

At Laurentian University, we recognize that work-life balance is essential for both personal well-being and professional success. Our policy offers employees some flexibility to better balance personal needs while maintaining effective service delivery.

Laurentian University is an inclusive and welcoming community committed to employment equity. Applications are encouraged from members of equity-seeking communities including women, racialized and Indigenous persons, persons with disabilities, and persons of all sexual orientations and gender identities/expressions. Laurentian University’s bilingualism policy provides a provision regarding the language requirement for persons self-identifying as First Nations, Métis or Inuit.

Laurentian University is committed to providing an inclusive and barrier-free experience to applicants with accessibility needs. Requests for accommodation can be made at any stage during the recruitment process. Please contact Human Resources for more information ([e-mail]).