Hamid Shayanfar is a Machine Learning Engineer at Canadian Global Maritime, where he applies optimization, artificial intelligence, and machine learning to automate and enhance engineering design processes. He develops AI algorithms for seabed boulder detection and C# optimization tools for mooring line design.
Before transitioning into machine learning, Hamid spent over five years as a Project Engineer at C-CORE, specializing in ocean and offshore engineering. His work supported Pre-FEED studies for offshore oil and gas projects, where he assessed environmental conditions, evaluated ice loads, and conducted risk assessments for offshore structures and subsea systems. During this time, he gained practical experience in data analysis, engineering programming, dynamic modeling, and structural analysis.
To complement his engineering background with advanced data skills, Hamid earned a Master of Data Science from Memorial University, supported by the techNL Making Waves Accelerator Scholarship. He also holds an M.Eng. in Mechanical Engineering from Memorial University, where he conducted experimental research on ice rubble beam bending at C-CORE, funded by Hibernia Management and Development Company Ltd. (HMDC).
His work has been recognized with several awards, including the Best Student Paper Award at the International Conference on Port and Ocean Engineering under Arctic Conditions, the RDC Ocean Industry Student Research Award, and Memorial University’s Recognition of Excellence Award. He also holds a Bachelor’s degree in Fluid Mechanics, with a final project on cavitation and drag-force measurement.