Postdoctoral Fellow Sessional Instructors: Department of Mathematics and Statistics

Postdoctoral Fellow Sessional Instructors:
Department of Mathematics and Statistics

The Department of Mathematics & Statistics in the Faculty of Science at the University of Calgary is inviting applications for the Fall 2026 - Winter 2027 Sessional Instructor ships for individuals applying to or holding a Postdoctoral appointment within the Department of Mathematics and Statistics.

Overview: The successful candidates will have responsibilities for lecture instruction, lab co-ordination, and any other associated duties, for up to two of these courses offered during the Fall 2026 and/or Winter 2027.

The Department of Mathematics & Statistics in the Faculty of Science at the University of Calgary is looking for instructors for the following courses. All course components will be delivered in-person.

• DATA 602 - Statistical Data Analysis
  :
  An introduction to the foundations of statistical inference including probability models for data analysis, classical and simulation-based statistical inference, and implementation of statistical models with R.
• DATA 603 - Statistical Modelling with Data
  :
  An introduction to the creation of complex statistical models, including exposure to multivariate model selection, prediction, the statistical design of experiments and analysis of data in R.
• MATH 211 - Linear Methods I:
  An introduction to systems of linear equations, vectors in Euclidean space and matrix algebra. Additional topics include linear transformations, determinants, complex numbers, eigenvalues, and applications.
• MATH 249 - Introductory Calculus
  :
  An introduction to single variable calculus. Limits, derivatives and integrals of algebraic, exponential, logarithmic and trigonometric functions play a central role. Additional topics include applications of differentiation, the fundamental theorem of calculus, improper integrals and applications of integration.
• MATH 265 - University Calculus I:
  An introduction to single variable calculus intended for students with credit in high school calculus. Limits, derivatives, and integrals of algebraic, exponential, logarithmic and trigonometric functions play a central role. Additional topics include applications of differentiation; the fundamental theorem of calculus, improper integrals and applications of integration. Differential calculus in several variables will also be introduced.
• MATH 275 - Calculus for Engineers and Scientists
  :
  An extensive treatment of differential and integral calculus in a single variable, with an emphasis on applications. Differentiation: derivative laws, the mean value theorem, optimization, curve sketching and other applications. Integral calculus: the fundamental theorem of calculus, techniques of integration, improper integrals, and areas of planar regions. Infinite series: power series, Taylor's theorem and Taylor series.
• MATH 277 - Multi-variable Calculus for Engineers and Scientists
  :
  An introduction to calculus of several real variables: curves and parametrizations, partial differentiation, the chain rule, implicit functions; integration in two and three variables and applications; optimization and Lagrange multipliers.
• MATH 375 - Differential Equations for Engineers and Scientists
  :
  Definition, existence and uniqueness of solutions; first order and higher order equations and applications;
  Homogeneous systems;
  Laplace transform; partial differential equations of mathematical physics.
• STAT 213 - Introduction to Statistics I:
  Introduction to probability, including Bayes' law, expectations and distributions. Discrete and continuous random variables, including properties of the normal curve. Collection and visual display of single and multi-dimensional data. Introduction to statistical modelling and estimation. Parametric and simulation-based confidence interval estimation.
• STAT 217 - Introduction to Statistics II
  :
  Parametric and simulation-based hypothesis testing, and associated errors. Confidence intervals and hypothesis testing for differences between two parameters, both parametric and simulation-based. Tests of association and goodness-of-fit. Statistical modeling and parametric testing of both the simple and multiple-model. Diagnostic…