Study or Work at the CNRG

Grad Student Applications

All Masters and PhD applications should go through the Department of Systems Design Engineering or the Cheriton School of Computer Science at the University of Waterloo (please contact Dr. Eliasmith to determine which is most suitable). To be certain that Dr. Eliasmith sees your application, mention him in your cover letter. Please feel free to contact Dr. Eliasmith if you have any questions not covered by information on this site.

It is preferred that students have a strong technical background, but this is not absolutely required, as long as the student is comfortable with formal/mathematical methods. Familiarity with and training in psychology, neuroscience, or biology is an asset but not essential.

Positions Available

For the most part, students choose their specific graduate project in consultation with Dr. Eliasmith (and consistent with the research framework used by the lab). That research approach is applicable to many different neural systems. To date, we have applied it to lamprey swimming, zebrafish swimming, rat navigation, basal ganglia function, working memory, auditory localization, visual processing, motor control, hemi-neglect, language processing, learning, statistical inference, etc. (see this page for currently on-going projects). Nevertheless, potential graduate student projects can be outside of this range of past applications.

However, occasionally well-defined projects are available. They will be listed here.

1. Automatic text understanding, clustering and classification (Ph.D.)

This project is focused on the development of new, state-of-the art methods for automatic text understanding. The project lies at the intersection of AI, Machine Learning, and Data Mining. It will result in tools that will be able to automatically determine the similarity between documents in an extremely large databases, and thus support automatic document classification. Specifically, the student will explore the development of a means of integrating semantic and syntactic information into a vector representation of text files for automatic classification and clustering. Computationally efficient algorithms (at worst linear) will be of most interest. The tools will eventually be used on a Beowulf supercomputing cluster. The project promises to be both exciting theoretically, and technically practical.