Post-Doctoral Fellowship in Methodological Projects Related to the Canadian Longitudinal Study on Aging (CLSA)
Drs. Mary Thompson and Changbao Wu at the Department of Statistics and Actuarial Sciences, University of Waterloo invite applications
for a post-doctoral fellowship to conduct methodological projects related to the CLSA. The CLSA is a population-based, 20-year, prospective cohort study. The 51,338 individuals comprising the CLSA are selected into one of two study components which use different
sampling designs; 21,241 participants, called the “Tracking” participants, were randomly selected from the 10 Canadian provinces and provided questionnaire data through and 30,097 participants were randomly selected from a geographically restricted area extending
25-50 km from one of 11 Data Collection Sites (DCSs) located across Canada. The Tracking participants provided questionnaire data through computer-assisted telephone interviewing (CATI) while the Comprehensive participants provided questionnaire data through
computer-assisted personal interviewing (CAPI) and provided additional physical measures at the Data Collection Site. The CLSA completed recruitment in 2015 and is currently 2/3 of the way through the first follow-up. The successful applicant will join members
of the CLSA Methodology Working Group to undertake projects such as identifying predictors of attrition, modelling measurement differences between the Tracking and Comprehensive Cohort, assessing optimal methods of imputation of systematically missing data.
The postdoc will be required to work at the University of Waterloo and at McMaster University (at the National Coordinating Centre of the CLSA).
The ideal candidate will have the experience and expertise to take advantage of the opportunity to access novel and rich data to build
a highly productive applied statistics portfolio.
The full-time temporary position is for one (1) year, with the possibility of extension subject to satisfactory performance evaluations.
We offer a competitive salary, equaling or exceeding CIHR fellowship stipends plus benefits. Actual salary will depend on the candidate’s
experience, qualifications and progress.
- PhD, or an equivalent doctoral degree in biostatistics or statistics completed within the last 5 years
- Experience conducting statistical analyses with large databases using SAS, Stata, or R
- Expertise in at least one of analysis of survey data, longitudinal studies, and causal inference
- Track record of research productivity
- Track record of, or strong potential for, independent funding
- Conduct data analyses at University of Waterloo in Waterloo, Ontario and McMaster University in Hamilton Ontario.
- Lead and co-author manuscripts for peer-reviewed journals
- Critically contribute to the CLSA Methodology Working Group efforts
- Actively participate in knowledge-transfer activities
- Perform limited administrative tasks
- Apply to external funding sources as eligible
Please send your application to Dr. Lauren Griffith (email@example.com)
and copy Dr. Mary Thompson (firstname.lastname@example.org)
as a single PDF file.
Application materials include i) a one-page cover letter describing career goals, research interests, and reasons for applying; ii)
your CV and graduate degree transcripts; iii) a reprint of your most significant first-author publication; iv) contact information for three (3) references; and v) proof of proficiency in English for candidates whose original language is not English, if applicable.
Application deadline is January 25, 2018. Expected start date is March 1 2018 or shortly thereafter, although this timing is flexible.
The position will remain open until a suitable candidate is found.
All applications are welcome but only potential candidates will be contacted.
The University of Waterloo respects, appreciates, and encourages diversity and is committed to accessibility for persons with disabilities. We welcome applications from all qualified individuals including women, members
of visible minorities, Aboriginal peoples and persons with disabilities.
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