Graduate Scholarships
Undergraduate Internships
Postdoctoral Fellowships
Research Topics of Interest

Who Can Apply?

Students who show a high degree of motivation in specializing in the field of mathematical and computational finance and business analytics, enrolled, or planning to enroll in a Masters or Doctoral degrees at: Université de Montréal, HEC Montréal, Concordia University, University of Calgary, University of Waterloo or Queen’s University.

When to Apply

Applications for graduate level scholarships are open until May 1st 2019.

Students can start participating the Fin-ML CREATE Program as of Fall 2019.

How to Apply?

The elements of the application are:

The application must be sent to as a single pdf file.


Students accepted in the Fin-ML CREATE program will receive - in total - a minimum of:

  • $17,500 for Masters students and $20,000 for Doctoral students

Note: These amounts include the internship remuneration. If the student is receiving an external award, the amount eligible through the Fin-ML CREATE grant can be adjusted.


Students will be expected to participate in the Fin-ML CREATE activities and fully benefit from the unique aspects of the CREATE training program.

Students will also be expected to do an internship which will be remunerated at the discretion of the hosting company, with a minimum of $10,000 for 12-weeks. The length of the internship can vary with the project. Assistance will be provided in order to find a suitable internship that answers the training requirements of the program. Students are also encouraged to find their own internships, subject to approval by the Fin-ML Internship Committee.


The Grant covers a period of one year, during which the student is expected to complete the CREATE Program requirements.

More information will be available soon

Contact us directly for more information at

A few examples of research opportunities where the Fin-ML CREATE program can thrive:

  • Predictive Modeling: Theoretical and applied research on predictive models in financial analytics in the fields of financial econometrics, neural networks, self-organizing maps and other machine learning algorithms that make use of the full potential of large data-bases of high-frequency data in order to develop ad-hoc solutions in finance;
  • Investment Strategies: Model design, optimization-based calibration, simulation, stress-testing and back-testing of investment strategies and algorithms operating in long-term horizons as well as at the microstructural level, thus including portfolio and structured products management, limit order book modeling and optimal intra-day trading strategies among other risk and investment questions, etc.;
  • Risk Management: Development of novel derivative pricing and hedging models for state-of- the-art risk and investment strategies; and
  • Automation: Applications of deep learning technologies such as NLP, image and text classification, question-answering algorithms, in automation in the financial and insurance sector.