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 observations - 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 researchers: - 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 documents. *** Meeting at NIPS *** 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 fairinternrecruit...@fb.com with a brief note confirming your submission. Please do not submit your application by email.
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