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. #J-18808-Ljbffr

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