Fully-Funded PhD in Quantum Computing, Quantum Chemistry, and Machine Learning at Université de Montréal Université de Montréal in Canada

This fully-funded PhD opportunity at Université de Montréal, in collaboration with Mila – Québec AI Institute, is designed for students passionate about quantum computing, quantum chemistry, and machine learning. The research focuses on learning density functional theory (DFT) on quantum computers, combining interdisciplinary approaches from quantum physics, computational chemistry, and artificial intelligence. The project aims to use quantum computers to generate quantum chemistry datasets and apply machine learning to develop advanced chemistry modeling tools. Initial work will involve comparing new representations of chemical Hamiltonians with existing strategies for quantum computers, leveraging recent advances in the field.

Supervision is provided by Prof Michel Côté (Physics), Prof Matthias Ernzerhof (Chemistry), and Prof Hlér Kristjánsson (Computer Science), all at Université de Montréal and Mila. There is also collaboration with Dr Simon Verret, a quantum developer at Université de Sherbrooke. The research environment is vibrant, with access to world-leading AI and quantum information expertise, and Montréal is recognized as a top student city with a rich cultural scene.

Applicants should have a Master’s degree in physics, chemistry, computer science, or a related field, and a solid background in quantum theory. Experience in computational quantum chemistry, programming (Python, C/C++, Fortran), DFT, quantum information, quantum computing, quantum field theory, or machine learning is advantageous. The position is fully funded and open to international students. The application deadline is December 1, 2025, and candidates should apply via the Mila supervision request system, listing both Prof Michel Côté and Prof Hlér Kristjánsson as supervisors and arranging for 2 or 3 letters of recommendation to be submitted directly.

For more information and to apply, visit the Mila website or the provided application link.