Fully Funded PhD in Quantum Computing, Physics, Chemistry, and Machine Learning at University of Montréal
A fully funded PhD position is available at the University of Montréal and Mila – Québec AI Institute for an interdisciplinary project at the intersection of quantum computing, quantum physics, quantum chemistry, and machine learning. The project, titled “Learning density functional theory on quantum computers,” aims to use quantum computers to generate quantum chemistry datasets and leverage machine learning to develop improved chemistry modeling tools. The research will involve comparing new representations of chemical Hamiltonians with existing strategies, building on recent advances in quantum algorithms for energy estimation in condensed matter and chemistry systems.
The PhD will be co-supervised by Prof Michel Côté (Physics), Prof Matthias Ernzerhof (Chemistry), and Prof Hlér Kristjánsson (Computer Science), all at the University of Montréal and Mila. There is also collaboration with Dr Simon Verret, a quantum developer at Université de Sherbrooke. The project offers a vibrant research environment in Montréal, a city known for its cultural diversity and academic excellence. The Department of Computer Science and Operations Research at the University of Montréal is highly ranked both nationally and internationally.
Applicants should hold a Master’s degree in physics, chemistry, computer science, or a related field, and possess working knowledge of quantum theory in condensed matter physics, quantum chemistry, or quantum information. Experience in computational quantum chemistry, programming (Python, C/C++, Fortran), DFT, quantum information and computing, quantum field theory, or machine learning is advantageous. The position is fully funded, though specific stipend and tuition details are not provided.
To apply, candidates must use the Mila supervision request system by December 1, 2025, listing both Prof Michel Côté and Prof Hlér Kristjánsson as potential supervisors. Two or three letters of recommendation must be submitted directly by referees through the Mila system. For more information, visit the provided links.
