Recruiting

Training programs offered

Open positions for Undergraduate – Interns – MSc – PhD – Postdoc

Are you interested in joining the MILA team for doing your Ph.D., your Master’s, a Postdoc, or a research internship? Good students are generally sought at all levels: undergraduate (part-time or interns), Master’s (and MSc-level interns), PhD (including visitors), and postdoctoral fellows.

Currently we are especially looking for post-doctoral fellows with a track record in deep learning.

We are also offering a Professional Masters in Machine Learning, combining specialized coursework with work experience in the artificial intelligence industry.

Recruitment questions should be addressed to .

IMPORTANT MESSAGE

YOU CAN CURRENTLY APPLY FOR

  • Post Doc
  • Interns
  • Professional Masters

APPLICATION PROCESS IS CLOSED FOR

  • Graduate students for Fall 2017 (open until December 31 2016)

The application process for 2018 Masters and PhDs will open on October 15 2017.

Application process

To initiate the application process for any of the positions, collect all the following and prepare to send them via email to :

  • prepare a message with a brief explanation of your interest in MILA or deep learning and pointing to relevant experience and learning (e.g. machine learning projects). This serves as your presentation/motivation letter. Also attach to this email:
  • Curriculum Vitae
  • All university-level transcripts,
  • Copies of no more than TWO reports or papers or theses,
  • 2 letters of recommendation (to be sent directly to the recruitment email by your referees using their institutional email, if possible),
  • Samples of code authored (either url to publically available repos or zip file with source code)

Just before sending your email, fill in the online MILA application form.

Once we receive your full application:

  • We will send a message acknowledging that we have received it (considering the high number of applications we receive, a delay could be expected)
  • A MILA prof 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 profs via skype or google-hangout.
  • We will inform you of our decision soon after the interview.

If you still have questions, you can reach Linda Peinthière at: 1 514 343-6111 ext. 1868.

If we accept you at MILA, congratulation! You passed the first and most difficult step. Then comes the official paperwork…

 

For doing your Master’s or PhD at University of Montreal

If you are going to do your Ph.D. or Master’s at MILA, once you got accepted at MILA, you have to officially apply at the University of Montreal for admission in the Computer Science Ph.D. or Master’s program within the UMontreal Computer Science Department (Département d’Informatique et recherche Opérationnelle).

1) Submit your application
https://admission.umontreal.ca/en/admissions/graduate-studies/submit-an-application/
Master program: 217510
PhD program: 317510

You will also have to send some accompanying original documents by snail mail

2) For PhDs only: Complete your part of this short form: http://diro.umontreal.ca/fileadmin/Documents/FAS/Informatique/Documents/3-Ressources-services/Ressources-formulaires/candphd.pdf sign it, scan it and email it to recruitment.at.mila AT gmail (dot) com and to the MILA prof who accepted to be your primary adviser. He will complete the rest of the form and give it to the department administration to have it deposited in your admission file.

3) Your adviser will also need to send to the department administration an acceptance letter stating that he agrees to supervise you during your Master’s or PhD program.

4) Urgently –  If you’re an international student (except for France), you have to apply for a scholarship for international student in order to pay the same tuition fees as the Canadian students (Bourse C). Do it as soon as possible! Fill part A of the form and send the scanned document to your adviser.

5) You should start the student permit / visa process as soon as possible (as it takes time to complete).

  • An essential document for this process will be your official admission letter from the University of Montreal (that will be sent to you once the admission decision has been officially pronounced by the administration).
  • Another important document will be a proof of sufficient funds. For this your adviser will have to send you a letter to attest that he will be supervising you and specifying your yearly funding.

For internships and Postdoc

These are considered paid work, so unless you are a Canadian citizen or permanent resident of Canada, you will need to quickly apply for an appropriate visa.

 

The MILA

MILA is the Montreal Institute for Learning Algorithms. We have seven machine learning faculty: Yoshua Bengio, Pascal Vincent, Aaron Courville, Simon Lacoste-Julien, Roland Memisevic (in CS), Chris Pal (at the polytechnique engineering school) and Laurent Charlin (at the HEC business school). These professors supervise approximately 90 students, post-docs and personnel. The institute is completely focused on deep learning.

 

Projects

Projects all involve deep learning, and range from natural language understanding to computer vision, through work on general-purpose advances in algorithms.

Students at PhD and postdoc levels are expected to already have strong exposure to machine learning, and preferably to deep learning. A strong background in mathematics (probability, linear algebra, numerical optimization, statistics) and computer science (numerical computation, open source software development) is expected at all levels. Postdoc levels candidates typically already have a strong track record in deep learning itself.

 

What does the institute study?

Faculty and students at MILA are primarily focused on studying deep learning. The institute is best known for:
Fundamentals of deep learning: the institute published one of the first papers on deep learning (NIPS’2006), a book (2009) and a review paper (2013), as well as many fundamental contributions regarding deep learning.

Autoencoders: the institute first became involved in the deep learning revolution by introducing the stacked denoising autoencoder. We also invented contractive autoencoders and generative stochastic networks.
Supervised neural nets: MILA pioneered the use of rectified linear units for feedforward neural nets. Deep rectifier nets have gained widespread popularity in industrial vision and speech recognition. We also developed the more recent maxout units, which have gained popularity in a variety of speech and vision applications.

Generative modeling: MILA is the birthplace of the spike-and-slab RBM, one of the best generative models of natural images. Students at MILA also study a wide range of topics related to Boltzmann machines, including better ways to train and sample from them.

Recurrent neural networks: Students at MILA study modeling temporal structure with recurrent nets. Neural language models (with word embeddings) were invented here, and we invented the RNN-RBM, a very successful model of polyphonic music.

We often compete in international competitions, and have recently won two transfer learning contests.

 

Industrial partnerships

The institute is sponsored by industrial partners, including:

  • IBM. MILA is doing fundamental research sponsored by IBM on various aspects of deep learning, especially for language-related tasks and computer vision.
  • D-Wave, The Quantum Computing Company™. MILA is developing applications of quantum computing to solve difficult learning and inference problems in probabilistic graphical models.
  • Ubisoft, a video game developer with a large studio in Montreal. MILA develops machine learning technologies to enhance many aspects of the gaming experience.
  • Google wants to build and train efficiently much larger models than what is currently possible and we have introduced the concept of distributed conditional computation and dynamically structured networks to achieve this objective.
  • Nuance cares about improvements in learning algorithms for speech and language.
  • Facebook cares about improvements in all aspects of deep learning.

 

Internships

The MILA hosts many interns and MILA students are also encouraged to do internships elsewhere. MILA students commonly do internships at top industrial research groups. Recent examples include:

 

  • David Warde-Farley did an internship with the deep learning infrastructure team at Google in Mountain View, CA during summer 2013.
  • Ian Goodfellow did an internship with the Street Smart team at Google in Mountain View, CA during summer 2013.
  • Yann Dauphin did an internship at Microsoft Research in Mountain View, CA during summer 2013.
  • Razvan Pascanu did an internship at Microsoft Research in Redmond, WA during summer 2013.
  • Yann Dauphin did an internship with the speech recognition team at Google in New York City during summer 2012.

Other questions

Do I need to speak French?

French is the official language of Quebec, and it is a good idea to learn French if you choose to live here, in order to experience to the fullest extent all that the city has to offer.

However, Montreal is one of the most bilingual locations on Earth, and many current students arrived at the institute with little or no proficiency in French.

The relative popularity of English and French at the institute fluctuates over time. Currently, many students in the institute originate from non-French-speaking countries such as the US, China, Turkey and Iran, so everyone in the institute speaks English reasonably well, whereas French is a common language to a smaller subset.

Courses at the University of Montreal are taught in French, but it is possible to take some of your required courses in English at McGill or Concordia, which are a short bus ride away from University of Montreal campus. Even at University of Montreal, it is preferred that students who speak French poorly turn in their assignments and exams written in English and can ask the professor for an English version of the questions (which the professor almost always accepts to provide). Undergraduate students must pass a French proficiency exam but Master’s and PhD students need not do so.

Will I need to pay extra fees as a foreign student?

By default, foreign students do need to pay extra fees, but MILA students are usually able to get these fees covered by one of the institute’s funding sources, or obtain an exemption. You will be notified whether you will have to pay foreign student fees before you need to formally accept or reject an offer of admission. Keep in mind that one needs to stay in good standing in terms of grades (and publications also help) to continue receiving the tuition fee scholarship provided by the university.

Isn’t it cold in Montreal?

It’s true that Montreal is cold in the winter (and, in fact, very hot in the summer), but the city is well-adapted to function in spite of the weather. Montreal’s snow removal is incredibly fast and efficient. You will need to get a good coat, hat, gloves, and boots, but most foreign students don’t find that they mind the cold too much, which is not very different from other cities such as Boston or Toronto.

The climate also provides certain advantages. There is an ice skating lake within walking distance of the institute and good skiing (both downhill and cross country) within reasonable driving distance of Montreal. It’s easy to go sledding in one of Montreal’s many parks — just grab a glossy cardboard box out of a recycling bin on the street and tear off one of its sides.

Why should someone with good math / systems CS skills work on deep learning?

Deep learning is an exciting new research area. It is young enough that a student with good mathematical aptitude can quickly learn most of the central ideas and begin contributing to the field. Recently, deep learning has proven to be a resource-intensive field, with large neural networks powered by cutting edge GPU-based or distributed implementations making great leaps forward in fields like object recognition and speech recognition.

Deep learning research presents an unparalleled opportunity for students with good mathematical skills and / or good CS implementation skills to really make a big impact.
Which professors are looking for students and what kind of projects do they want to work on?

MILA has five CS faculty members:

one faculty at the Engineering school, specializing in machine learning for computer vision:

and one faculty in the business school (HEC) specializing in decision and recommendation algorithms:

 

Feel free to email any of them directly to inquire about possible projects.

What have alumni of the institute done with their degrees?

  • Pascal Vincent obtained his PhD at MILA and is still a member of MILA. He is now an Assistant Professor.
  • Hugo Larochelle obtained his PhD at MILA and is now an Assistant Professor at the University of Sherbrooke.
  • Dumitru Erhan obtained his PhD at MILA and is now a software engineer with Google’s visual search team in Venice, CA near Los Angeles.
  • James Bergstra obtained his PhD at MILA and is now a post-doctoral research scientist at the University of Waterloo.
  • Nicolas Le Roux obtained his PhD at MILA and is now Scientific Program Manager at Criteo.
  • Nicolas Chapados obtained his PhD at MILA and now works at ApSTAT, a company he co-founded with other members of MILA.
  • Olivier Delalleau obtained his PhD at MILA and now works at Ubisoft in Montreal.
  • Philippe Hamel obtained his PhD at MILA and now works at Google in Mountain View, CA.
  • Guillaume Desjardins obtained his PhD at MILA and is now a Senior Researcher at Google Deep Mind in London.
  • Razvan Pascanu recently obtained his PhD at MILA and is now a Senior Researcher at Google Deep Mind in London.
  • Ian Goodfellow recently obtained his PhD at MILA and is now a Research Scientist with Google’s deep learning infrastructure team in Mountain View, CA.

 

What do current students think of the institute?There are many advantages to being a student at MILA:

  • Vibrant intellectual research environment: at MILA we are all excited to advance the state of artificial intelligence. At any given day you can easily find students discussing all kinds of ambitious research ideas. The professors are very involved and engaged with the students, making the research process very open and interactive.
  • Quebecois work/life balance: at MILA we do good research, but we don’t work ourselves to death doing it. The culture here is much more laid back than it is at other labs, especially in the US, that publish at the same conferences.
  • Lots of computational resources: University of Montreal has the second largest research budget of any university in Canada, and the MILA has a stellar track record of attracting grant money. This means MILA students have access to plenty of fast computers with modern GPUs. The institute’s resources are supplemented with access to shared computer clusters across Quebec and Canada in general.
  • Funding for conferences: So long as we have the funding, which we usually do, MILA sends a large delegation to the top machine learning and deep learning conferences like NIPS, ICML, and ICLR. It’s common for Master’s students, second authors, third authors, and even students who haven’t had a paper accepted to get to go to a conference. Professors here strongly encourage attending conferences as an integral part of the life of a researcher. This compares favorably to many other labs where students are discouraged from taking time away from research or where there is rarely funding to send many students.
  • Research-based funding: MILA students are generally paid scholarships that are competitive with top US graduate programs. This funding is usually contingent on working on research projects related to one of the lab’s many industrial projects. These projects are often publishable. Most students find this arrangement preferable to some of the arrangements at other universities, where grad student funding is often tied to large amounts of non-research-related work, such as TAing.