Master of Engineering in Materials Science and Engineering - Data Analytics and Machine Learning

The Department of Materials Science and Engineering offers graduate programs leading to the degrees of Master of Applied Science (MASc), Master of Engineering (MEng), and Doctor of Philosophy (PhD). Graduate courses and research opportunities are offered to qualified students in a wide range of subjects. The department covers a diverse range of research areas in the structure, processing and characterization of metal alloys, advanced ceramic coatings, semiconductor devices, nanocomposites and biomaterials. Also, there is a strong emphasis on simulation and computational modelling.Typical subjects in chemical metallurgy involve a study of the equilibria existing during the reduction of oxides with carbon and metals, properties of iron and steelmaking slags, kinetics of high-temperature reactions, mathematical modelling of metallurgical processes, process metallurgy, and hydrometallurgy. Typical physical metallurgy and materials science subjects deal with the structure and properties of metallic, ceramic, and polymeric materials in such fields as plastic deformation, surface properties, electron microscopy, biomaterials, nuclear materials, nanocomposites, metallic glasses, corrosion, fatigue, welding and bonding, phase transformations and solidification.These studies are all related to the general problem of understanding structure-property-processing relationships in materials, the design of advanced materials and devices, and developing highly-efficient, environmentally-responsible materials processing and manufacturing that considers life cycle analysis and material recycling.This program provides you with advanced professional training in materials engineering. You can obtain an industry-oriented, post-graduate degree at Canada's top engineering school in as little as one year's time.Data Analytics involves the algorithmic processing of data for purposes of description (what happened), prediction (what will happen), or prescription (what should be done). Machine Learning focuses on the computational and statistical methods for learning patterns and associations and obtaining insights from data. Data Analytics and Machine Learning collectively represent the forefront of technology innovation powering a wide range of applications including personalized e-commerce, cybersecurity, intelligent logistics and scheduling, financial investing, digital marketing, adaptive user interfaces, and health applications including medical imaging analysis.

A Bachelor of Applied Science (BASc) in Engineering or Bachelor of Engineering (BEng) with a minimum of B (73%+) over the final two (2) years of an undergraduate program from an accredited institution. GRE scores are NOT required. Do NOT submit these scores. Two academic references are required. Curriculum Vitae (C.V.) A statement is required for MASc and PhD applications but is not required for MEng applications.