Develop Python pipelines for uncertainty quantification and machine learning research as a part time Research Assistant at McGill University. Work at Burnside Hall under a professor’s supervision.

The Department of Mathematics and Statistics seeks a Research Assistant 2 to support statistical modelling, experimentation, and analysis. The work includes active-learning strategies, reliable predictions, risk control, scientific LLM agents, and machine-learning-guided materials design. A PhD in a related field is an asset.

Key Responsibilities:

- Develop methods for uncertainty quantification and calibration
- Design active-learning strategies and experiments
- Maintain Python pipelines for statistical modelling
- Contribute to publications, presentations, and grant reporting




- Develop retrieval and memory methods for scientific LLM agents

Requirements:

- Knowledge of machine learning and probabilistic modelling
- Understanding of model interpretability and uncertainty quantification
- Experience in machine-learning-guided materials design or optimization
- Peer-reviewed publications demonstrating relevant research experience
- Proficiency in Python and scientific machine-learning tools

Apply your Python, machine learning, and uncertainty quantification expertise to research supporting reliable predictions at McGill University.

📌 AI Machine Learning Research Assistant (Burnside)
🏢 McGill University
📍 Burnside