Research assistant - casual position
Start date: April - May 2026
Application deadline: Will remain until filled
Supervisor: Dr. Jason Bernard
Faculty: Faculty of Science and Technology
Novel Particle Swarm Optimization Topology and Adaptive Behaviour
Overview
Optimization problems involving find the best solution from a list of all feasible options. Such problems are ubiquitous both in our day-to-day lives and in research. While commonplace, they are challenging due to so-called “curse of dimensionality”, which causes the list of options to explode in size as the number of factors increases. For example, a vehicle’s navigation system when plotting a route considers not just the distance from your location to the destination, but local traffic density, expected traffic density, road conditions, and weather to name a few. Just the number of feasible routes from your location to a destination can be numerous and adding in other considerations greatly increases the complexity of the problem. Optimization algorithms are a machine learning approach for searching through the possible options efficiently to find an optimal (or sufficient) solution.
One optimization algorithm is particle swarm optimization (PSO), which is inspired by how birds flock. It consists of a population of particles that move freely through an N-dimensional space, where each dimension represents an element of the solution (e.g., routing information or weather). PSO is often improved by applying a swarm topology (a method for organizing the placement of particles) and adaptive behaviours.
This research proposes to improve efficiency and effectiveness for solving optimization problems using PSO, while also simplifying parameterization. This will be done by investigating new swarm topologies and adaptive behavior.
Specific activities include, but are not limited to
- Developing Python code with a focus on particle swarm optimization.
- Conducting literature reviews concerning particle swarm optimization.
- Development and execution of test cases.
- Data analysis of the results from test cases.
Qualifications
- Must have excellent knowledge of particle swarm optimization including topologies and adaptive behaviors.
- Strong Python programming skills.
- Prior research experience is an asset.
- Strong logical reasoning and analytical skills preferred
This is a funded position and the successful candidate will be offered to co-author research papers. Research assistants will receive training and guidance on: 1) conducting a literature review, 2) methodological research on algorithms, and 3) analysis, and 4) paper writing.
How to apply
Please send your CV to cbernard@athabascau.ca along with an a short email highlighting how your skill set aligns with the qualifications.
Athabasca University and the researchers are committed and seek to support equity in employment and research opportunities. We strongly encourage applications from Indigenous people, people of colour, people with disabilities, 2SLGBTQ+ people, women, and other historically marginalized groups. Applicants are welcome, but not required, to self‐identify in their letter of application.
For more information on this Research Assistant Opportunity, please contact cbernard@athabascau.ca Applications will be accepted until a suitable candidate is found.
Dr. Jason Bernard
Assistant Professor
School of Computing and Information Systems
Faculty of Science and Technology
Athabasca University
cbernard@athabascau.ca