Undergraduate Research Opportunities

Undergraduate Research Assistantships (URAs)

The Undergraduate Research Assistantship (URA) program is a competitive program that provides summer research opportunities to undergraduate students with demonstrated financial need.

Job postings are typically made available in mid-January.

For information on postings and how to apply, visit the URA information page from Student Financial Services.


NSERC's Undergraduate Student Research Awards (USRAs)

Undergraduate Student Research Awards (USRAs) are meant to stimulate students' interest in research in the natural sciences and engineering. They are also meant to encourage a student to undertake graduate studies and pursue a research career in these fields. If students would like to gain research experience in an academic setting, these awards can provide them with financial support through their host university.

Job postings are typically made available in mid-January.

For information on postings and how to apply, visit the USRA information page from Student Financial Services.


Advanced Research Project (MATH/STAT*4600)

Each semester, undergraduate students have the opportunity to complete mathematics and statistics research projects with faculty members. This is a terrific opportunity to work on an advanced project that matches your interests and goals. Speak to your instructors about project opportunities and find a research supervisor. You may then register for either MATH*4600 (Fall or Winter) or STAT*4600 (Fall or Winter). These are both 1.00 credit courses.

MATH*4600 Projects

  • Extracting E.coli growth parameters from impedance measurements
    (Valerie Hodgins, Fall 2016, Supervisor: Hermann Eberl)
    Impedance microbiology is an experimental technique that is based on the observation that certain electrophysical properties of a medium in which bacteria grow  are altered by microbial activity. Aim of this project was to develop a mathematical model that allows to  determine bacterial growth curve parameters from impedance measurements,  and to use it to compare the growth rates of pathogenic and nonpathogenic E.coli strains. This involved methods from ordinary differential equations, biomathematics, optimization, statistics, and programming in R.
  • Detecting entanglement in Hankel and Toeplitz density matrices
    (Matthew Kazakov, Fall 2016, Supervisor: Rajesh Pereira)
    Quantum entanglement is an inherent trait to the universe that has, and continues to, elude mathematicians and physicists. Originally appearing in the equations brought forth by Einstein, and several other quantum pioneers, entanglement is the phenomena where linking two (or more) particles appears to result in an instantaneous exchange of information. This is something that goes against the logic of there being a finite upper bound on the rate at which communication can occur, which is what Einstein and physicists alike got tangled up in. Measuring or altering one part of the entangled system seems to instantly affect other parts of the system.
    Mathematically, entanglement can be modelled in the following way; suppose that a Hilbert space  is composed of two subspaces, label them  and  such that . A pure state   is unentangled (or equivalently; separable, factorable, etc.) if  for  and . If  cannot be written as a single tensor product, then the state contains some measure of entanglement. Similarly regarding mixed states, a density matrix  is separable if it can be written as a summation of tensor products, i.e.  for some matrices  and  within the subsystems of.
    This project focused on the analysis of Hankel and Toeplitz density matrices and it was shown that they are positive partial transpose (PPT) and in the case of Hankel matrices, they fulfill the definition of what it means to be separable.

STAT*4600 Projects

  • Monitoring fork lengths of Athabasca white sucker: A simulation study
    (Neil Faught, Fall 2016, Supervisor: Lorna Deeth)
    Researchers are currently sampling various species of fish from the Athabasca River in northern Alberta to determine what effects, if any, oil sands development has had on their populations. As of now, researchers have analyzed yearly physiological measurements of sampled fish to see if any change has occurred their physiology that may be a result of human activity. As there are many statistical monitoring methods available for performing such a task, this report applied a handful of such methods (such as moving average and exponentially weighted moving average models) to simulated data to determine what model-parameter set combination was most successful at detecting changes in this simulated data. Scoring metrics had to be developed to evaluate the performance of each model-parameter set combination tested.
  • Estimating Abundance of Seabirds
    (Denys Kelly, Winter 2014, Supervisor: Julie Horrocks)
    Abundance estimation of species is becoming increasingly important with the world’s rapidly changing environment. This project investigated the effects of various environmental and geographic variables on the abundance of two species of seabirds, the Northern Gannet (NOGA) and the Greater Shearwater (GRSH), from 2008 to 2013. A seabird observation dataset provided by Environment Canada’s Canadian Wildlife Service program, Eastern Canada Seabirds at Sea (ECSAS) was restructured and merged with environmental data available from Oregon State University. A variety of statistical models were fit to these data and the following environmental and geographical variables were found to be important: latitude, longitude, distance from shore, bathymetry, primary production and the time of year. Models were then used predict abundance of seabirds at specific latitude and longitudes.