The School of Mathematical and Computational Sciences invites applications from highly motivated and technically skilled candidates for a Research Assistant (Statistical Methodology) to support a funded NSERC Discovery Grant. This position will contribute to advanced methodological research in statistical inference for two-phase and multi-phase sampling designs and partially observed data, with a primary focus on theoretical development, simulation-based evaluation, and preparation of peer-reviewed publications.

This is a research-intensive role requiring independent judgment, advanced statistical training, and the ability to integrate theory, computation, and scholarly writing.

RESPONSIBILITIES:

• Conduct independent methodological research in statistical inference, including the development and evaluation of estimators and sampling-design strategies for two-phase and multi-phase studies under missing or partially observed data
• Contribute to theoretical and asymptotic derivations related to statistical efficiency, influence functions, and variance estimation
• Design, implement, and analyze Monte-Carlo simulation studies to assess the bias, variance, and efficiency of proposed methods
• Develop and maintain reproducible research code in R to support methodological and simulation-based work
• Prepare, revise, and submit manuscripts to peer-reviewed statistical journals, including responding to referee and editorial comments
• Use real datasets, where appropriate, to illustrate and validate proposed statistical methodology
• Work independently to manage research tasks and timelines, with periodic consultation with the Principal Investigator

QUALIFICATIONS:

• Completed MSc in Statistics, Biostatistics, or Mathematical and Computational Sciences with a specialization in statistics
• Graduate-level training in statistical theory, including inference and asymptotic methods
• Demonstrated experience with statistical methodology for two-phase or multi-phase sampling designs, including the development or evaluation of estimators under missing or partially observed data
• Experience conducting simulation-based methodological studies
• Demonstrated ability to produce scholarly written work suitable for peer-reviewed publication (e.g., MSc thesis, draft manuscripts, or published work).
• Proficiency in R for statistical computing and reproducible research
• Ability to work independently and manage technically complex research tasks.
• Strong written and verbal communication skills

Assets:

• Experience with influence-function-based methods, semiparametric estimation, or efficiency theory
• Experience converting graduate thesis research into peer-reviewed journal publications