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.
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📌 Ai Machine Learning Research Assistant Burnside
🏢 McGill University
📍 Burnside