The Cheriton School of Computer Science is looking for exceptional students currently enrolled in a Computer Science program or related areas who have a keen interest in research and in pursuing graduate studies.

Students will participate in a four-month, full time, research-based co-op term to work directly with a faculty supervisor in a particular research area (e.g., Computer Security, AI, Human-Computer Interaction, Theoretical Computer Science, etc.).

  • Learn the research methodology related to a particular field of interest (see projects below)
  • Review existing literature and develop new research questions.
  • Work with a supervisor and a team of graduate students to address the research questions, which may involve developing systems, creating algorithms, solving mathematical problems, designing experiments, etc.
  • Work on publications to disseminate research findings to relevant academic conferences.

Meet Dmitry

Dmitry is a URF award recipient and spent the Fall 2020 term under the supervision of Professor Joanne Atlee and Michael Godfrey on the Rex project. The key in Rex lies in minimizing the information extracted from source code, thereby allowing for the analysis of very large systems. The analyses themselves involve discovering paths within the “fact based” graph extracted by Rex. Dmitry’s role revolved around improving the accuracy of these analyses by incorporating control-flow information into the extracted facts. Dmitry shares the highlight of his experience working on the Rex project and how it compared to past co-op positions.

dmitry_kobets

“I think my favourite part about the internship was the amount I learned. Reflecting back to my previous co-ops, I think this was probably the most independence and ownership I was ever given on any project. As a result, I had to very quickly and very thoroughly learn my problem’s domain such that I could make progress. I learned a lot about the compiler tools involved in static analysis, such as ASTs and CFGs, and I came to realize that I really enjoy static analysis work. Apart from interesting technical knowledge, I also learned a great deal about abstract problem solving, since that was a huge component of my daily routine. I learned that open-ended problem solving is something that I find very fulfilling, and the new skills that I gained from it will definitely help me in future work.” Dmitry Koberts

View past URF award winners


Application Deadline

Term Deadline
Winter 2023 Tuesday, September 20, 2022 (9:00 am)
Spring 2023 TBD
Fall 2023 TBD

Compensation

URF recipients will receive a minimum of $12,000/term:

  • $7,500 contributed by the Cheriton School of Computer Science
  • Remaining provided by faculty supervisor (and/or NSERC USRA, Faculty of Mathematics MURA)

Eligibility

  • Students who are currently in 3rd or 4th year are eligible to apply; exceptional students from earlier terms will also be considered.
  • A cumulative average of at least 80% is required.
  • Preference is given to students enrolled in the Computer Science major or related programs (i.e. any program that would prepare a student for Computer Science graduate studies). 
  • The faculty supervisor must have an appointment or cross-appointment in the School of Computer Science

A student can only do one URF/Co-op per term, and must be on a co-op term or otherwise not taking courses. Note that International Students need a SIN# and work permit to work in Canada. URF recipients normally work on campus at the University of Waterloo, but due to pandemic restrictions, some or all positions may be remote. If remote, it is expected that students will be working from a location within Canada.

Open and Sponsored Applications 

There are two types of URF applications: 

  • A sponsored application means you and a faculty member already have a connection, you've already discussed a specific project to work on, and they're providing a reference letter that states that they'll hire you as a research assistant if you're awarded a URF.   

  • An open application means you want to be matched with a faculty supervisor. Potential supervisors list projects near the bottom of this page, and you will indicate your interest in specific projects in your application. 

How to apply

The application has two parts: an online information form and supporting materials submitted to a secure file server.  

Information Form 

The information form gathers basic information and provides some additional checks that your application is submitted correctly. It should be completed when you’re ready to submit the single PDF with your cover letter, resume, and transcript (see next part).   

[ Information Form

Supporting Materials  

1. A single PDF containing a cover letter, resume, and transcript. Your PDF file must have the filename “YourFirstName_YourLastName.pdf” and it must contain these items in the following order: 

  • Your cover letter should be 1 to 2 pages with: your research interests; why you want to pursue a URF; what your future plans are for graduate school; why you're interested in working on the specific project(s) you selected or the project you're already sponsored for. 

  • Your resume should be up-to-date and highlight relevant skills and experience. 

  • Your transcript(s) should be current and list all of your undergraduate courses and grades. 

2. A letter of recommendation from a professor (or equivalent person with authority).  Each letter must be a PDF file with the filename “YourFirstName_YourLastName_ReferenceLastName.pdf”.  

  • For open applications, a letter of recommendation from a professor is optional, (but encouraged). 

  • For sponsored applications, a letter of recommendation from the sponsoring faculty member is required.     

All supporting materials must be submitted to this secure file server: https://vault.cs.uwaterloo.ca/s/g8R9mtt5GEE6EgQ   

Please note that incomplete applications (i.e., missing any of the required items above) will not be considered.

Open Projects for Winter 2023

Computational Statistics in Computer Graphics 
Supervisor: Toshiya Hachisuka
Statistical approaches have been used in computer graphics for many different problems. In particular, in light transport simulation for photorealistic image synthesis, stochastic estimation using Monte Carlo methods is the most popular and efficient approach. This project aims to investigate mathematically and numerically the effectiveness of specific statistical methods for problems in computer graphics and to discover more efficient computational methods. The student is expected to study existing statistical methods such as Monte Carlo methods, experiment with them by coding a test program in computer graphics and work on its mathematical formulation to understand why and how well it works on a problem in computer graphics. 
Operating system kernels - Theory vs. Practice
Supervisor: Martin Karsten
Operating system kernels are fairly big and very complicated software entities that address complex resource management challenges and typically support a massive set of hardware devices. Meaningful research into the structure and performance of operating systems is hampered by a significant barrier to entry: A research operating system must support a reasonable set of modern hardware devices to obtain useful performance measurements beyond simplistic benchmark tests. The overall goal of this project is lowering that barrier to entry by building a simple kernel nucleus and combining it with 3rd-party open-source software to support a large variety of device drivers. The critical next step is hollowing out an existing open-source operating system kernel and making the hardware support components independent of the core generic resource management services. This will result in a novel open-source research platform that enables subsequent studies on structural and algorithmic innovations for operating system kernels.