Grant appointment until December 31, 2027 This position is designated as one that requires a variable work schedule. However, it is understood that your work week will consist of 35 hours.
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: https://nohfc.ca/en/pages/programs/people-talent-program/workforce-development-stream
Vacancy Status: New position
The Research Intern will contribute to an applied research project integrating advanced numerical modelling and artificial intelligence for sustainable mining applications. The primary research will focus on developing and validating numerical models of tire-road interaction and tire-induced microplastic generation under underground mining conditions. The intern will conduct numerical simulations, generate and organize structured simulation datasets, perform sensitivity and uncertainty analyses, validate model outputs, and prepare the resulting database for AI-based predictive modelling. The intern will also contribute to the development and evaluation of data - driven predictive approaches and to the interpretation of research findings related to environmental sustainability, occupational exposure, and sustainable underground mining.
- Develop and calibrate numerical models using Particle Flow Code (PFC), Fast Lagrangian Analysis of Continua (FLAC), and other appropriate numerical modelling tools to simulate tire-road interaction, tire wear, and particle detachment under underground mining conditions.
- Design and conduct systematic parametric simulations under varying operational and environmental conditions, including vehicle loads, speeds, roadway gradients, tire properties, surface roughness,moisture conditions, and other relevant parameters.
- Generate, organize, process, and compile numerical simulation outputs into a structured and well-documented database suitable for artificial intelligence and predictive modelling applications.
- Conduct sensitivity and uncertainty analyses to identify the key parameters controlling tire wear, microplastic generation, and particle behaviour.
- Review relevant standardized wear-test frameworks, including ASTM G65, ISO 4649, and ASTM D2228, and incorporate appropriate parameters into numerical modelling and validation activities.
- Validate numerical model outputs using benchmark tests, published experimental data, and relevant scientific literature to ensure physical consistency, reliability, and reproducibility.
- Prepare simulation datasets and relevant input and output variables for subsequent development and evaluation of AI-based predictive models.
- Contribute to the development, evaluation, and interpretation of data-driven and AI-assisted predictive approaches using the generated numerical simulation database.
- Document numerical modelling and data-analysis workflows, model configurations, assumptions, datasets, metadata, and computational procedures to support reproducibility and future research development.
- Prepare technical summaries, data visualizations, progress reports, research presentations, and materials supporting scientific publications and knowledge-transfer activities.
- Contribute to the interpretation of numerical and data-driven results in relation to sustainable underground mining, environmental pathways, worker exposure, ventilation, and related occupational and environmental considerations.
- Perform other duties as assigned.
- Master's degree in Mining Engineering or a closely related engineering or applied-science discipline.
- Graduate-level research experience involving numerical modelling, computational simulation, artificial intelligence, machine learning, or a closely related field.
- Demonstrated ability to conduct independent technical research and analyze complex engineering datasets.
- A minimum of one (1) year of relevant research or technical experience in numerical modelling, computational simulation, artificial intelligence, machine learning with mining engineering applications.
- Experience acquired through a master's thesis, graduate research project, research assistantship, or equivalent technical research may be considered relevant experience.
- Strong knowledge of numerical modelling and computational simulation methods applicable to mining engineering applications.
- Experience with Particle Flow Code (PFC), Fast Lagrangian Analysis of Continua (FLAC), or comparable numerical modelling platforms.
- Knowledge of model development, calibration, validation, sensitivity analysis, and uncertainty analysis.
- Ability to design and execute systematic parametric simulations and manage large volumes of simulation outputs.
- Knowledge of structured data generation, data processing, database development, and management of computational research datasets.
- Knowledge of artificial intelligence and machine-learning concepts and their application to engineering datasets.
- Experience with programming and data-analysis tools applicable to artificial intelligence, machine learning, numerical analysis, and scientific computing is considered an asset.
- Ability to prepare numerical simulation datasets for data-driven and predictive modelling applications.
- Familiarity with model training, validation, performance evaluation, and interpretation of predictive models is considered an asset.
- Knowledge of underground mining, geomechanics, tire-road interaction, sustainable mining, environmental assessment, or occupational exposure is considered an asset.
- Strong analytical, quantitative, research, and problem-solving skills.
- Strong technical writing, documentation, data-visualization, and scientific communication skills.
- Ability to work independently and collaboratively within an interdisciplinary 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 [email protected]***
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 ([email protected]).
