The 2023 DLRL Summer School will be held in Montréal, presented by CIFAR and Mila, in collaboration with Amii and the Vector Institute.

Registration for the DLRL Summer School is $700 CAD and must be paid within 30 days of acceptance. Students must cover their own travel and accommodation costs. Support for students who self-identify as Black or Indigenous is available — see the “CIFAR Inclusive AI Scholarship” information below.

Applications closed

Important Dates

January 19, 2023 Applications open for 2023 DLRL Summer School
March 8, 2023 Deadline to apply for 2023 DLRL Summer School
Week of April 10, 2023 Notice of acceptance and invitation to register
May 5, 2023 Last date to register
July 17–21, 2023 2023 DLRL Summer school in session


The DLRL Summer School uses a scoring system to select the majority of participants. Applicants with a higher score have a higher probability of being accepted. The scoring system favours applicants who are graduate students that have not attended the DLRL Summer School in the past and whose research areas are closer to the scope of the summer school. Additional consideration is given to applicants who identify as a member of an under-represented group in AI.

CIFAR Inclusive AI Scholarship

CIFAR is pleased to launch the CIFAR Inclusive AI Scholarship this year, which will be awarded this year to all students who are admitted into the CIFAR DLRL Summer School who self-identify as Black or Indigenous. The scholarship will cover registration fees, transportation costs, meals, and accommodations for the duration of the Summer School, in accordance with CIFAR’s travel policy.

Please note:

  • Trainees who applied for previous years’ DLRL Summer Schools but were not successful will be required to reapply.
  • The DLRL Summer School sessions will take place in English only, to accommodate the widest number of international students.

Who Should Apply?

Graduate students, postdocs, and early-career researchers in quantitative disciplines, who have some knowledge of machine learning, deep learning, and reinforcement learning. We welcome students from all around the world, with a focus on inclusion of those who identify as members of underrepresented groups in AI.

Ready to apply?

To apply for the Deep Learning + Reinforcement Learning Summer School, you will be taken to our online platform, SurveyMonkey Apply. Please use the instructions below to get started on your application. If you have not used this portal before or you don't see the “CIFAR Deep Learning + Reinforcement Learning Summer School” card when you click programs, you will have to fill out an eligibility profile.

Step 1: Click “Fill Out Eligibility Profile” if this option is presented, or else do steps 2, 3, 4 to update the eligibility profile.


Step 2: Click name
Step 3: Click on “account”
Step 4: Click “eligibility”
Step 5: Update your eligibility profile
Step 6: Select the Deep Learning + Reinforcement Learning Summer School option to continue your application.

Stream Selection Form

Applications closed


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“The #DLRL Summer School is a place of learning at the highest scientific level in world-changing research areas but also an opportunity for networking, creating connections which can stay for life and fuel future collaborations.”

Yoshua Bengio

Canada CIFAR AI Chair, Mila | Co-Director, CIFAR Learning in Machines & Brains Program |
Scientific Director, IVADO | Scientific Director, Mila | Professor, Université de Montréal
Elissa Strome, Associate Vice-President, Research & Executive Director of the Pan-Canadian AI Strategy at CIFAR

“The Summer School has become a beacon for top AI students and experts from around the world. We’re excited to showcase what makes Canada a global leader in the field of AI.”

Elissa Strome

Executive Director,
Pan-Canadian AI Strategy at CIFAR
Kyunghyun Cho

“I attended DLRL early in my career. The lectures were amazing and provided an opportunity to have close interaction among the participants, and it was at that point that I decided I wanted to continue to do research full time in the area of deep learning.”

Kyunghyun Cho

Associate Professor, New York University; Fellow in the CIFAR
Learning in Machines & Brains program, CIFAR Azrieli Global Scholar 2017-2019

“Beyond the learning and networking opportunities, [the #DLRL Summer School] showcases the rich AI ecosystem we enjoy in Canada. It's been a major factor in encouraging some of the brightest minds to launch their research careers here.”

Graham Taylor

CIFAR Learning in Machines & Brains;
Canada CIFAR AI Chair, Vector Institute, University of Guelph
Alona Fyshe

“[The #DLRL] is a wonderful opportunity to bring the AI leaders of tomorrow together for a fun and immersive learning experience. It’s also a great way to showcase the depth of AI knowledge and expertise that exists in Canada.”

Alona Fyshe

CIFAR Learning in Machines & Brains;
Canada CIFAR AI Chair, Amii, University of Alberta
CIFAR Azrieli Global Scholar 2016-2018