Research Assistant 2

McGill University; Burnside; On-site; CAD 37,000 - 52,000; Part time

Job summary

McGill University’s Department of Mathematics and Statistics is seeking a part-time researcher to develop methods for uncertainty quantification and calibration, ensuring reliable predictions and risk control. The role includes overseeing active-learning experiments, building Python pipelines for modelling and analysis, and contributing to publications and grant reporting.

We expect a PhD in Statistics, Electrical Engineering, Materials Science, Mechanical Engineering, or a related field, with

Qualifications

• PhD in statistics, engineering, or related field; asset if AI/ml related.
• Knowledge of ML, probabilistic modelling, uncertainty quantification, and interpretability.
• Experience in ML-guided design or optimization with peer-reviewed publications.
• Proficiency in Python and scientific ML tools; reproducible research software development.

Responsibilities

• Develop methods for uncertainty quantification and calibration to ensure reliable predictions.
• Oversee active-learning strategies and experiments to improve data efficiency.
• Develop Python pipelines for statistical modelling, experimentation, and analysis.
• Contribute to scientific publications, conference presentations, and grant reporting.
• Develop verifiable retrieval and memory methods for scientific LLM agents.

Skills

Python programming

Machine learning

Uncertainty quantification

Reproducible research

Education

PhD in Statistics

PhD in Electrical Engineering

PhD in Materials Science

PhD in Mechanical Engineering

Tools

Python

Job description

Please refer to the How to Apply for a Job (for External Candidates) job aid for instructions on how to apply.If you are an active McGill employee (ie: currently in an active contract or position at McGill University), do not apply through this Career Site. Login to your McGill Workday account and apply to this posting using the Find Jobs report (type Find Jobs in the search bar).Hiring Unit:Department of Mathematics and StatisticsPosition Summary:Develop methods for uncertainty quantification and calibration to ensure reliable predictions and risk control.Oversees and assists with the design of active-learning strategies and experiments to improve data efficiency and guide experimental decisions.Develop and maintain Python pipelines for statistical modelling, experimentation, and analysis.Contribute to scientific publications, conference presentations, and grant reporting.Develop verifiable retrieval and memory methods for scientific large language model (LLM) agents.Qualifying skills:A PhD in Statistics, Electrical Engineering, Materials Science, Mechanical Engineering, or a related field, with research experience in AI or machine learning, is an asset.Knowledge of machine learning, probabilistic modelling, uncertainty quantification, and model interpretability.Experience in machine-learning-guided materials design or optimization, demonstrated through peer-reviewed publications.Proficiency in Python and scientific machine-learning tools, including reproducible research software development.Experience and Education:A PhD in a related field is an asset.Before applying, please note that to work at McGill University, you must be both authorized to work in Canada and willing to work in the province of Quebec at the campus where the position is based / located.McGill University is an English-language university where most teaching and research activities are conducted in the English language, thereby requiring English communication both verbally and in writing. The level of English required for this position has been assessed at a level # 4 on a scale of 0-4.Hourly Salary:$32.15Hours per Week:5 (Part time)Location:Burnside HallSupervisor:ProfessorPosition Start Date:2026-10-13Position End Date:2027-02-26Deadline to Apply:2026-10-29This position is covered by the Association of McGill University Research Employees (AMURE) collective agreement.McGill University hires on the basis of merit and is strongly committed to equity and diversity within its community. We welcome applications from racialized persons/visible minorities, women, Indigenous persons, persons with disabilities, ethnic minorities, and persons of minority sexual orientations and gender identities, as well as from all qualified candidates with the skills and knowledge to productively engage with diverse communities. McGill implements an employment equity program and encourages members of designated groups to self-identify. Persons with disabilities who anticipate needing accommodations for any part of the application process may contact, in confidence, [e-mail] .