SUBMIT A POST-DOC REQUEST FOR SUPERVISION
To study at Mila, you must first 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.
- Complete the online Mila Supervision Request form.
- We will send a message acknowledging that we have received the application.
- A Mila professor will examine your application (we receive numerous applications so this may take a couple of weeks, please be patient.)
- 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.
- We will inform you of our decision soon after the interview.
- 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 admission process, start the visa process as soon as possible (as it takes time to complete).
If you still have questions, you can reach Linda Peinthière at: 1 514 838-6452 poste 105
Open post-doc position :
Yoshua Bengio is looking for a 2-yr post-doc sponsored by Recursion to investigate exploration and knowledge-seeking learning algorithms in drug discovery based on causal interventions and high-throughput biology (hundreds of thousands of images per week of cells receiving different treatments and analyzed via their visual appearance post-treatment). The ideal candidate has a combination of experience and interest in this topic (and generally in ML for understanding biology and drug discovery) as well as an established track record in deep learning / reinforcement learning. Please fill the form here and write to Yoshua.
Other open post-doc positions especially for these particular topics (although strong candidates on other topics will also be considered):
– Deep learning for positive social impact: applications in healthcare, humanitarian applications, the environment, etc.
– Ethically aligned AI
– 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)