Graduate studies | Our programs | Doctor of Philosophy
Program Details
PhD in Actuarial Science
Who is this program for?
This program is for students who have completed a Master’s or Bachelor's degree in statistics, actuarial science, mathematics, or other quantitative discipline and would like to pursue research in actuarial science. Graduates from this program typically enter academia or work in the private sector after graduation.
What does it take to get in?
A Master's degree in statistics, actuarial science, mathematics, or other quantitative discipline completed or expected with a cumulative GPA of at least 78% from a Canadian university (or its equivalent). A demonstrated ability to conduct high level research is helpful but not required.
Students with a four-year Bachelor's degree are invited to apply directly to the PhD program. The admissions committee will review each applicant's background for suitability and may recommend admission to our Master's program instead.
When should I apply?
Application deadline: January 15th for fall admissions. There are no Winter or Spring admissions.
PhD in Biostatistics
Who is this program for?
This program is for students who have completed a Master’s or Bachelor's degree in statistics, actuarial science, mathematics, or other quantitative discipline that are looking to develop research skills to address a wide range of challenges arising in health, biology, and the environment. Demand for biostatisticians in Canada and internationally is high and graduates typically find employment in areas such as pharmaceutical companies, contract research organizations, regional or national centers for health research, academic centers, and public health agencies.
What does it take to get in?
A Master's degree in statistics, actuarial science, mathematics, or other quantitative discipline completed or expected with a cumulative GPA of at least 78% from a Canadian university (or its equivalent). A demonstrated ability to conduct high level research is helpful but not required.
Students with a four-year Bachelor's degree are invited to apply directly to the PhD program. The admissions committee will review each applicant's background for suitability and may recommend admission to our Master's program instead.
When should I apply?
Application deadline: January 15th for fall admissions. There are no Winter or Spring admissions.
PhD in Statistics
Who is this program for?
This program is for students who have completed a Master’s or Bachelor's degree in statistics, actuarial science, mathematics, or other quantitative discipline. The PhD will build on this background with the goal of training first-class independent researchers. Graduates from this program find career opportunities in academia, research, and private industry.
What does it take to get in?
A Master's degree in statistics, actuarial science, mathematics, or other quantitative discipline completed or expected with a cumulative GPA of at least 78% from a Canadian university (or its equivalent). A demonstrated ability to conduct high level research is helpful but not required.
Students with a four-year Bachelor's degree are invited to apply directly to the PhD program. The admissions committee will review each applicant's background for suitability and may recommend admission to our Master's program instead.
When should I apply?
Application deadline: January 15th for fall admissions. There are no Winter or Spring admissions.
Research Areas
Actuarial Science
- Capital allocation
- Finance
- Portfolio optimization
- Pricing and hedging of investment-linked products
- Risk management
- Ruin theory
- Stochastic modelling
- Monte Carlo and quasi-Monte Carlo
Biostatistics
- Analysis of life history data
- Causal inference
- Clustered data
- Design and analysis of clinical trials
- Epidemiological methods
- Event history analysis
- Generalized linear models
- Longitudinal data analysis
- Methods for dealing with incomplete data and measurement error
- Stochastic processes
- Statistical computing
- Studies of biological systems
Statistics
- Artificial intelligence
- Biostatistics
- Business and industrial statistics
- Computational statistics
- Data science
- Exploratory data analysis
- Machine learning
- Non-parametric methods
- Probability theory
- Statistical computing
- Statistical modeling and inference
- Statistical geometry
- Stochastic processes
- Survey methods
- Survival analysis
- Time series