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Karim Zaghib

2 months ago

PhD in Explainable AI for Resilient Energy Optimization and Storage at Concordia University Concordia University in Canada

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded PhD with tuition coverage and a competitive stipend. The fellowship is listed as CAD 35,000 per year for 4 years.

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Country

Canada

University

Concordia University

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Keywords

Computer Science
Electrical Engineering
Chemical Engineering
Materials Science
Energy Storage
Predictive Analytics
Explainable Ai
Renewable Energy Systems

About this position

PhD opportunity at Concordia University in Explainable AI for Resilient Energy Optimization and Storage (EXAIREOS), part of the Volt-Age Living Lab.

The project is supervised by Prof. Karim Zaghib in the Department of Chemical and Materials Engineering at Concordia University, Montreal, Canada. It focuses on battery energy storage, renewable energy integration, solar energy systems, energy management, and explainable artificial intelligence (XAI) for building energy optimization and decarbonization.

Research topics include battery modeling and optimization, battery degradation and lifecycle analysis, state estimation (SOC/SOH), photovoltaic-battery co-optimization, renewable forecasting, intelligent energy management, and AI-powered simulation and decision-support tools. The successful candidate will work with real building energy data, weather data, electricity tariffs, and renewable generation profiles, and will collaborate with multidisciplinary researchers and industry partner Énergère.

Funding: fully funded PhD with tuition coverage and a competitive stipend of CAD 35,000 per year for 4 years.

Eligibility: applicants should have a master's degree in a relevant engineering or closely related field, with strong preparation in battery systems, renewable energy, modeling/simulation, optimization, Python or MATLAB/Simulink, and preferably machine learning or XAI.

Start date: Fall 2026. Applications: rolling review.

How to apply: send a single PDF with letter of intent, CV, unofficial transcripts, referee names/emails, publications, and supporting documents to [email protected]. Use the subject line EMS_Your name.

Funding details

Fully funded PhD with tuition coverage and a competitive stipend. The fellowship is listed as CAD 35,000 per year for 4 years.

What's required

Applicants should have a master's degree in Electrical Engineering, Energy Engineering, Chemical Engineering, Computer Engineering, Materials Engineering, or a closely related discipline. Strong background in battery energy storage systems, renewable energy systems, energy management, or electrochemical modeling is required. Knowledge of lithium-ion batteries, battery degradation mechanisms, battery lifecycle analysis, or battery management systems is expected. Experience with mathematical modeling, simulation, optimization, or predictive analytics for energy systems is required. Proficiency in Python, MATLAB/Simulink, or similar scientific computing and data analysis tools is required. Experience with machine learning, artificial intelligence, explainable AI (XAI), data-driven modeling, photovoltaic systems, distributed energy resources, energy storage optimization, or microgrid technologies is an asset. Strong analytical, problem-solving, research, communication, and teamwork skills are expected, along with interest in sustainable energy systems, electrification, renewable integration, and AI-enabled decision-support technologies.

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