FAIR Paris

In 2015, Facebook AI Research (FAIR) Paris was created to advance the field of artificial intelligence through open research for the benefit of all.

Facebook is seeking Research Interns to join Facebook AI Research (FAIR) Paris. We are committed to advancing the field of artificial intelligence by making fundamental advances in scientific methods and technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as deep learning, computer vision, audio and speech processing, natural language processing, machine learning, reinforcement learning, computational statistics, and applied mathematics. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale. Many of our interns publish at top tier conferences such as NIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, NAACL, and EMNLP. This internship has a minimum 12-week duration beginning in April. Internships will be awarded on a rolling basis and candidates are encouraged to apply early. Applicants are expected to be pursuing a Msc in Computer Science.

Facebook is seeking PhD students to join FAIR Paris on a 3-year fixed term CIFRE contract to work in collaboration with a public research lab. PhD students who are passionate in areas such as deep learning, CV, NLP, machine learning, reinforcement learning, computational statistics, and applied mathematics are invited to join.

Internships

(Continual) Reinforcement Learning

We propose to develop new models able to learn policies when facing a stream of tasks to solve, in a lifelong learning setting. Particularly we want to study neural architecture search methods with the objective is to obtain a growing neural network able bboth to solve new task easily, but also to keep memory of past task.

Content tracing

This intern will be related to the problematic of watermarking, or more generally of content tracing with neural networks. A good entry point is our paper "Radioactive data: tracing through training" at ICML 2020's key principles of openness, collaboration, excellence, and scale, we make big, bold research investments focused on building social value and bringing the world closer together.

Reinforcement learning

The internship will cover topics related to exploration in reinforcement learning. Notably, we would like to study how to integrate expert demonstrations in the exploration process to speed up convergence in problems with sparse reward or even just for imitation learning when no reward is available.

Academic Programs & Partnerships

Facebook supports exciting & innovative research through meaningful engagements, including partnerships with the Ministry for Higher Education and Research, and the National Association for Research and Technology (ANRT).

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