PhD Student in AI, Energy Systems and Battery Modeling
Top Benefits
About the role
Supervisor: Dr. Karim Zaghib Department: Chemical and Materials Engineering, Gina Cody School of Engineering and Computer Science University: Concordia University, Montreal, Canada Start Date: Fall 2026 PhD fellowship: 35K CAD per year for 4 years Project: EXAIREOS Description: AI and modeling project
PROJECT OVERVIEW
EXAIREOS (Explainable Artificial Intelligence for Resilient Energy Optimization and Storage) is a Living Lab collaboration between Concordia University and Énergère that aims to develop an AI-powered decision-support platform for building energy optimization and decarbonization. The project integrates battery energy storage, solar energy systems, and explainable artificial intelligence to simulate energy performance, optimize renewable energy integration, and support data-driven decision-making. The successful candidate will contribute to battery modeling, energy storage optimization, and AI-enabled simulation tools that improve system performance, sustainability, and resilience for municipalities, industry, and communities across Canada.
ROLE TASKS
Conduct research on battery energy storage modeling and intelligent energy management for renewable energy systems. Develop physics-based and data-driven models to simulate battery charging, discharging, degradation, and lifecycle performance. Design and optimize battery management strategies that improve system efficiency, reliability, and longevity. Develop predictive models for battery degradation, state estimation (SOC/SOH), and lifecycle analysis under varying operating conditions. Design and evaluate co-optimization strategies for photovoltaic (PV) generation and battery energy storage systems. Integrate battery storage models into an AI-powered decision-support platform for energy system simulation and optimization. Support the development and validation of explainable AI algorithms for renewable energy forecasting, storage optimization, and operational decision-making. Assemble, test, and validate proof-of-concept solar–battery systems using experimental and simulated data. Analyze real-world building energy data, weather information, electricity tariffs, and renewable generation profiles to improve energy management models. Collaborate with multidisciplinary researchers and industry partners to develop scalable, explainable, and sustainable energy solutions. Publish research findings in leading peer-reviewed journals and present results at international conferences. REQUIREMENTS
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. Knowledge of lithium-ion batteries, battery degradation mechanisms, battery lifecycle analysis, or battery management systems (BMS). Experience with mathematical modeling, simulation, optimization, or predictive analytics for energy systems. Proficiency in Python, MATLAB/Simulink, or other scientific computing and data analysis tools. Experience with machine learning, artificial intelligence, explainable AI (XAI), or data-driven modeling is considered an asset. Familiarity with photovoltaic systems, distributed energy resources, energy storage optimization, or microgrid technologies is considered an asset. Experience analyzing large datasets, developing simulation models, and validating predictive algorithms. Strong analytical, problem-solving, and research skills, with the ability to work independently and collaboratively in multidisciplinary teams. Excellent written and verbal communication skills, with demonstrated interest in publishing high-quality scientific research. Interest in sustainable energy systems, electrification, renewable integration, and AI-enabled decision-support technologies.
HOW TO APPLY
Please send the following documents in a single PDF file to volt-age.recruitment@concordia.ca: Letter of intent clearly aligned with the professor’s research domain (You may also review their recent publications and highlight relevant experience.) Academic CV Unofficial transcripts with CGPA and course names Names and emails of 3 referees Publications with embedded links, if any Any other supporting documents that strengthen your application
Subject of the email: EXAIREOS_Your Name
About Concordia University
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PhD Student in AI, Energy Systems and Battery Modeling
Top Benefits
About the role
Supervisor: Dr. Karim Zaghib Department: Chemical and Materials Engineering, Gina Cody School of Engineering and Computer Science University: Concordia University, Montreal, Canada Start Date: Fall 2026 PhD fellowship: 35K CAD per year for 4 years Project: EXAIREOS Description: AI and modeling project
PROJECT OVERVIEW
EXAIREOS (Explainable Artificial Intelligence for Resilient Energy Optimization and Storage) is a Living Lab collaboration between Concordia University and Énergère that aims to develop an AI-powered decision-support platform for building energy optimization and decarbonization. The project integrates battery energy storage, solar energy systems, and explainable artificial intelligence to simulate energy performance, optimize renewable energy integration, and support data-driven decision-making. The successful candidate will contribute to battery modeling, energy storage optimization, and AI-enabled simulation tools that improve system performance, sustainability, and resilience for municipalities, industry, and communities across Canada.
ROLE TASKS
Conduct research on battery energy storage modeling and intelligent energy management for renewable energy systems. Develop physics-based and data-driven models to simulate battery charging, discharging, degradation, and lifecycle performance. Design and optimize battery management strategies that improve system efficiency, reliability, and longevity. Develop predictive models for battery degradation, state estimation (SOC/SOH), and lifecycle analysis under varying operating conditions. Design and evaluate co-optimization strategies for photovoltaic (PV) generation and battery energy storage systems. Integrate battery storage models into an AI-powered decision-support platform for energy system simulation and optimization. Support the development and validation of explainable AI algorithms for renewable energy forecasting, storage optimization, and operational decision-making. Assemble, test, and validate proof-of-concept solar–battery systems using experimental and simulated data. Analyze real-world building energy data, weather information, electricity tariffs, and renewable generation profiles to improve energy management models. Collaborate with multidisciplinary researchers and industry partners to develop scalable, explainable, and sustainable energy solutions. Publish research findings in leading peer-reviewed journals and present results at international conferences. REQUIREMENTS
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. Knowledge of lithium-ion batteries, battery degradation mechanisms, battery lifecycle analysis, or battery management systems (BMS). Experience with mathematical modeling, simulation, optimization, or predictive analytics for energy systems. Proficiency in Python, MATLAB/Simulink, or other scientific computing and data analysis tools. Experience with machine learning, artificial intelligence, explainable AI (XAI), or data-driven modeling is considered an asset. Familiarity with photovoltaic systems, distributed energy resources, energy storage optimization, or microgrid technologies is considered an asset. Experience analyzing large datasets, developing simulation models, and validating predictive algorithms. Strong analytical, problem-solving, and research skills, with the ability to work independently and collaboratively in multidisciplinary teams. Excellent written and verbal communication skills, with demonstrated interest in publishing high-quality scientific research. Interest in sustainable energy systems, electrification, renewable integration, and AI-enabled decision-support technologies.
HOW TO APPLY
Please send the following documents in a single PDF file to volt-age.recruitment@concordia.ca: Letter of intent clearly aligned with the professor’s research domain (You may also review their recent publications and highlight relevant experience.) Academic CV Unofficial transcripts with CGPA and course names Names and emails of 3 referees Publications with embedded links, if any Any other supporting documents that strengthen your application
Subject of the email: EXAIREOS_Your Name