PhD Student Position – AI for Smart Energy Systems

Carleton University

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Description

Carleton University is seeking a driven and qualified Ph.D. candidate to join a dedicated research group starting in either Fall 2026 or Spring 2027. This research position centers on utilizing Artificial Intelligence (AI) to optimize smart energy systems, specifically focusing on microgrids and nanogrids.

The selected student will dive into advanced technical domains, exploring Deep Reinforcement Learning (DRL), Multi-Agent Reinforcement Learning (MARL), Large Language Models (LLMs), and Agentic AI. The ultimate goal of this research is to improve renewable energy integration, manage battery storage and electric vehicles, coordinate demand response, and secure resilient cyber-physical grid operations. Joining this program allows the researcher to address vital sustainability challenges while developing high-level expertise in both academic and industrial AI applications.

Qualification & Candidate Profile

Ideal candidates must possess a solid academic foundation and a deep interest in cutting-edge computational modeling for energy infrastructures. The position is tailored for individuals who meet the following requirements:

• Educational Background: A strong degree in Electrical Engineering, Computer Engineering, Computer Science, Artificial Intelligence, Machine Learning, Control Systems, Power Systems, or a closely matching discipline.
• Technical Skills: Practical experience using Python alongside deep learning libraries such as PyTorch or TensorFlow is highly desirable. Prior exposure to optimization techniques and reinforcement learning frameworks is also a significant asset.
• Preferred Experience: Special preference will be granted to applicants who have an established research background in multi-agent systems, large language models, energy management networks, microgrids, or practical power applications.

Application Process

Interested individuals who want to leverage AI for next-generation energy solutions should submit their application materials directly to the supervising faculty member via email.

• Contact Email: Send your application to shichaoliu@cunet.carleton.ca
• Required Documents: You must attach a comprehensive Curriculum Vitae (CV) that highlights any relevant publications, along with copies of your unofficial undergraduate and graduate academic transcripts.

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