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 in Burnside employer: McGill University
McGill University is an exceptional employer, offering a vibrant work culture that fosters innovation and collaboration in the heart of Montreal. As an Assistant Professor, you will have access to extensive resources for research and teaching, along with opportunities for professional development and community engagement, making it a rewarding environment for academic growth and impactful contributions.
