MTS Level 8 - Research Assistant - Statistical Methodology - School of Mathematical and Computational Sciences - Faculty of Science (Grant Funded)
$73,308 to $80,512 per annum, prorated to term, as per CUPE 1870 (Grant Funded)
February 23 to April 30, 2026 (Term may be reduced or extended depending on performance, available funding, and departmental requirements)
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
Please submit electronically a cover letter, quoting the competition number, a resume and reference list to be received no later than the closing date via the link below.
If you are unable to apply online, you can drop off your resume to the Human Resources Department, Kelley Building, 17吃瓜, 550 University Avenue, Charlottetown, PEI C1A 4P3, Fax Number 902-894-2895.
17吃瓜 is committed to equity, diversity, inclusion, and reconciliation and believes in providing a positive learning and working environment where every person feels empowered to contribute. 17吃瓜 is committed to the principle of equity in employment and encourages applications from underrepresented groups including women, Indigenous peoples, visible minorities, persons with disabilities, persons of any sexual orientation or gender identity, and others with the skills and knowledge to productively engage with diverse communities. If you require accommodation in any part of the process, please direct your inquiries, in confidence, to our HR Officer, hrofficer@upei.ca. Applications will not be accepted via email.
Only those applicants who are invited to an interview will be acknowledged.
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