Intern - FAC Electrical

Deploying and calibrating PD sensors across critical electrical assets to enable real-time condition monitoring. Collecting and preprocessing PD data, including signal filtering, noise reduction, and timestamp alignment. Applying advanced analytics techniques such as machine learning, statistical modeling, and pattern recognition to identify early signs of insulation degradation or equipment failure. Developing dashboards and visualization tools to present PD trends, anomalies, and actionable insights to engineering and maintenance teams. Collaborating with cross-functional teams to validate findings and recommend maintenance actions based on data-driven insights The student will have the opportunity to learn and apply industrial data analytics, modeling, simulation and optimization skills to solve the practical issues and bring real impact on the Electrical system. The intern will also have opportunity to participate to an end-to-end solution development team to contribute ideas and learn from industrial subject experts. Deliverable: A working PD monitoring system with data analytics tools and a dashboard for real-time insights. Currently pursuing a diploma or degree in Electrical Engineering, Data Science, or a related field. Strong analytical and problem-solving skills, with an interest in predictive maintenance and industrial applications. Proficiency in programming languages such as Python, R, or MATLAB for data analysis and visualization. Familiarity with data analytics techniques including signal processing, machine learning, and statistical modeling. Ability to work with large datasets, including data collection, cleaning, and interpretation. Good communication skills and the ability to collaborate effectively in cross-functional teams.