Apply

The 2024 DLRL Summer School will be held in Toronto, Ontario, Canada, presented by CIFAR and the Vector Institute, in collaboration with Amii and Mila.

Registration for the DLRL Summer School is $800 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.

Costs for successful applicants based in Switzerland to attend DLRLSS (including registration fees, transportation costs and accommodations) will be covered under the partnership between CIFAR and the Swiss National Science Foundation (SNSF).

Important Dates

January 9, 2024 Applications open for 2024 DLRL Summer School
February 16, 2024 Deadline to apply for 2024 DLRL Summer School
Week of March 7, 2024 Notice of acceptance and invitation to register
April 29, 2024 Last date to register
July 8-17, 2024 2024 DLRL Summer school in session

Selection

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 in-person 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 equity-deserving community in AI.

CIFAR Inclusive AI Scholarship

We’re pleased to offer the CIFAR Inclusive AI Scholarship, awarded 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, up to a total of $3,750 CAD.

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.

SurveyMonkey

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

pattern-5x5-2

Stay up to date on the DLRL Summer School

Stay up to date on Canadian AI news, events and research

Need more info?

testimonial-yoshua-bengio

“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
Kimberly Nestor

“Attending DLRL 2023 was such an enriching summer school experience! I had a great time academically and it allowed me to expand my knowledge about the field. I made lifelong friends and connections, while learning about potential internships and postdoctoral opportunities.”

Kimberly Nestor

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
D'jeff Kanda Nkashama

“What struck me the most was the commitment of the DLRL summer school organizers to foster inclusive AI. As a member of a minority visible group, I was fortunate to receive the CIFAR Inclusive AI scholarship that enabled me to participate fully in this enriching program. Throughout the summer school, I delved into various aspects of machine learning, spanning optimization, computer vision, natural language processing, as well as ethical AI and AI safety. The breadth and depth of knowledge covered were truly impressive.”

D'jeff Kanda Nkashama

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
circle-single-110px