Facebook AI Research (FAIR) Residency Program 1147 views

The Facebook AI Research (FAIR) Residency Program is a one-year research training program with Facebook’s AI Research group. The program is designed to give practical experience of machine learning research.

About the program:

• The selected candidate will be paired with a senior researcher or engineer in FAIR, who will act as mentor.
• Candidate and mentor together will pick a research problem of mutual interest and then devise new deep learning techniques to solve it.
• Collaborations beyond the assigned mentor are also encouraged.
• The research will be communicated to the academic community by submitting papers to top academic venues (NIPS, ICML, ICLR, CVPR, ICCV, ACL, EMNLP etc.), as well as open-source code releases. Visit the FAIR research page for examples of research performed in FAIR.
• This is a research residency experience is designed to prepare you for graduate programs in machine learning, or to kickstart a research career in the field.
• This is a full-time program that cannot be undertaken in conjunction with university study or a full-time job.

Requirements:

• Candidates who have a strong technical background and are passionate about AI research are encouraged to apply.
• Applicant with prior experience in machine learning is certainly a strength but people from a diverse range of backgrounds, including areas ostensibly unrelated to machine learning such as (but not limited to) math, physics, finance, economics, linguistics, computational social science, and bioinformatics are also welcome to apply.
• Selected candidates will be based in Facebook Menlo Park and New York locations.

Tasks and Responsibilities:

• Learn how to perform research in deep learning and AI.
• Understand prior work and existing literature.
• Work with research mentor(s) to identify problem(s) of interest and develop novel AI techniques.
• Translate ideas into practical code (in frameworks such as PyTorch, Caffe 2).
• Write up research results in the form of an academic paper and submit to a top conference in the relevant area.

Eligibility:

• Applicant must hold Bachelor’s degree in a STEM field such as Mathematics, Statistics, Physics, Electrical Engineering, Computer Science, or equivalent practical experience.
• Applicant must have completed coursework in: Linear Algebra, Probability, Calculus, or equivalent.
• Applicant must have some coding experience in a general-purpose programming language, such as Python or C/C++; familiarity with a deep learning platform such as PyTorch, Caffe, Theano, or TensorFlow.
• Applicant must be able to communicate complex research in a clear, precise, and actionable manner.

Applications:

Applicant need to complete the application and submit the following items (in PDF format):

• CV/Resume (including links to GitHub, professional webpages, publications, or blogposts as applicable)
• Personal statement (maximum of 2 pages)
• Academic grade transcript