[AI*IA] Internship at Reinforcement Learning and Robot Learning Groups at FaceBook AI Research (FAIR), Menlo Park

Internship at Reinforcement Learning and Robot Learning Groups at 
FaceBook AI Research (FAIR), Menlo Park, CA

The Reinforcement Learning Group and Robot Learning Group at Facebook AI 
Research (FAIR) at Menlo Park are looking for interns to work on a range 
of problems in the areas of reinforcement learning, bandit algorithms, 
deep learning, robot learning, and recommendation systems. The interns 
will be supervised by researchers in the group who have excellent 
publication record with dozens of papers at top-tier machine learning, 
AI, and robotics conferences and journals in recent years. Our research 
topics include:

*** Topics of Interest ***
- Reinforcement Learning (RL): Model-based RL, Hierarchical RL, Robust 
RL, Safe RL, Risk-sensitive RL, Exploration in RL, Multi-task and 
Transfer learning in RL, Multi-agent RL, Imitation and Apprenticeship 
Learning, Simulation to real problem, Control with high-dimensional 
- Off-policy Evaluation and Causal Inference
- Multi-armed Bandits and Online Learning
- Personalized Recommendation / Large-scale Recommender Systems
- Real-time planning and control
- AutoML, Robust optimization, Bayesian optimization, and 
multi-objective optimization

The internship will be in Menlo Park, California, at the heart of the 
Silicon Valley. The duration of the internship is 12-16 weeks and it can 
start any time from April 1, 2019. Candidates who pass the interview 
will be mentored and work closely with one or more of the following FAIR 

    Yuandong Tian (https://yuandong-tian.com)
    Mohammad Ghavamzadeh (http://chercheurs.lille.inria.fr/~ghavamza)
    Roberto Calandra (https://www.robertocalandra.com/about/)
    Franziska Meier (https://am.is.tuebingen.mpg.de/person/fmeier)
    Jakob Foerster (http://www.jakobfoerster.com )
    Akshara Rai (http://www.cs.cmu.edu/~arai/)

*** Requirements ***
Eligible applicants can be at any level, however, especial preference 
will be given to Master’s and Ph.D. students in Computer Science, 
Statistics, Operations Research, Applied Mathematics or related fields, 
with a strong background in AI, machine learning, and good programming 
skills. We are particularly interested in candidates with prior exposure 
to deep learning, optimization, statistics, reinforcement learning, 
bandits, and scalable machine learning.

*** Application Submission ***
FAIR hires interns on a rolling basis, however for this call, we 
strongly recommend the applicants to submit their application as soon as 
possible and no later than January 15, 2019. The screening of the 
candidates is done at a first-come-first-serve basis. No more 
applications for this particular team will be accepted once the 
positions are filled, though other teams within FAIR will continue 
hiring on a rolling basis. The application should include a brief 
description of the applicant’s research interests and past experience, 
plus a CV that contains the degrees, GPAs, relevant publications, name 
and contact information of up to two references, and other relevant 

*** Meeting at NIPS-2018 ***
Yuandong Tian, Mohammad Ghavamzadeh, and Roberto Calandra are going to 
be at NIPS-2018 in Montreal and will be able to meet with potential 
candidates there.

*** To Apply ***
Please send your application through the Facebook Careers posting at: 
https://www.facebook.com/careers/jobs/2061911867158321/. After you have 
submitted your application through the Facebook Careers website, please 
send an email to fairinternrecruiting@fb.com with a brief note 
confirming your submission. Please do not submit your application by email.