Research Assistant 2

McGill University; Montreal; On-site; GBP 20,000 - 28,000; Part time

Job summary

McGill University in Burnside Hall is seeking a part-time researcher to develop methods for uncertainty quantification and to contribute to data-driven experiments. You will design active-learning strategies, build Python pipelines for modelling, and engage in publications and grant reporting.

The role emphasizes ML, probabilistic modelling, materials-informed optimization, and reproducible research. English-language university environment; start date 2026-10-13 with a 2027-02-26 end date.

Qualifications

• PhD in a related field is an asset.
• Knowledge of ML, probabilistic modelling, uncertainty quantification, and interpretability.
• Experience in ML-guided materials design or optimization with publications.
• Proficiency in Python and reproducible research tools.

Responsibilities

• Develop methods for uncertainty quantification and calibration to ensure reliable predictions and risk control.
• Oversee and assist 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.

Skills

Machine learning knowledge

Probabilistic modelling

Uncertainty quantification

Model interpretability

Python proficiency

Education

PhD in Statistics, EE, Materials Science, ME, or related

Job description

Hiring Unit:

Department of Mathematics and Statistics

Position 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:

$32.15

Hours per Week:

5 (Part time)

Location:

Burnside Hall

Supervisor:

Professor

Position Start Date:

2026-10-13

Position End Date:

2027-02-26

Deadline to Apply:

2026-10-29

This position is covered by the Association ofMcGill 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].