SUPERVISION REQUEST PROCESS FOR POSTDOCS

Mila > SUPERVISION REQUEST PROCESS FOR POSTDOCS

SUBMIT A POST-DOC REQUEST FOR SUPERVISION

To study at Mila, you must be supervised by a professor who is a member of Mila.The requests for supervision are made via a Mila application form, while the registrations are administered by the universities themselves.

  1. Complete the online Mila Supervision Request form.
  2. We will send a message acknowledging that we have received the application.
  3. Mila professors will have access to your application but we suggest you directly write to the targeted professors to check on their needs and interests.
  4. If your application is selected at this stage you will then be invited to an oral interview with one or two Mila professors via Skype or Google-Hangout.
  5. We will inform you of our decision soon after the interview.
  6. Post-doc is considered as paid work, so unless you are a Canadian citizen or permanent resident of Canada, after being accepted through the Mila supervision request process, start the visa process as soon as possible (as it takes time to complete).

If you still have questions, please contact supervision@mila.quebec

OPEN POSITIONS

Post-doc positions especially for these particular topics (although strong candidates on other topics will also be considered), please contact specific professors for details:

  • Causal discovery of cell dynamics – Mila & Helmholtz International lab (choose Yoshua Bengio as advisor)
  • AI for drug discovery
  • Deep learning for positive social impact: applications in healthcare, humanitarian applications, the environment, etc.
  • Ethically aligned AI
  • System 2 deep learning
  • Deep learning for medical imaging
  • Combining deep learning, unsupervised learning and reinforcement learning to discover explanatory factors of the environment
  • Deep learning for understanding natural language and for dialogue
  • Applications of deep learning in industry
  • The intersection of deep learning and computational neuroscience, especially biologically plausible deep learning-like mechanisms
  • Structured prediction, optimization and theory of deep learning (choose Simon Lacoste-Julien as advisor)
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