Research Intern – AI-Numerical Modelling Sustainable Mining, School of Engineering and Comp

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

Position No.: JOBPOST-

Salary: $39,960 per year

Competition ends: Tuesday, September 8th at 4:00 pm

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.

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…