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Post-doctoral Researcher, Cognitive Science and Machine learning (PhD Grad)

  • CDD
  • Paris
  • Publié il y a 1 an
  • Les candidatures sont actuellement fermées.

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Our Brain & AI team at FAIR is based in Europe and is looking for a postdoc to join us. Our research aims to identify the similarities and differences between the computations of the human brain and those of modern deep learning algorithms. We focus on self-supervised learning applied to the domain of speech and language, and its link to neurosciences. The candidate should have a solid experience in machine learning, signal processing, speech processing and as well as in neuroscience, as testified by first-author publications in this domain. We are looking for candidates that are finishing their Ph.D.

Post-doctoral Researcher, Cognitive Science and Machine learning (PhD Grad) Responsibilities:

  • Collaborate on research to advance the science and technology of intelligent machines
  • Devise better data-driven models of human behavior
  • Influence progress of relevant research communities by producing publications
  • Collaborate and increase productivity on existing FAIR projects as a contributing team member

Minimum Qualifications:

  • Currently has or is in the process of obtaining a PhD degree in Computer Science or related field or relevant experience
  • Track record of publications that demonstrate experience in a quantitative domain
  • Experience collaborating within a team to solve analytical problems using quantitative approaches
  • Ability to manipulate and analyze high-volume, high-dimensionality data from varying sources
  • Ability to communicate research for public audiences of peers.
  • Knowledge in a programming language
  • Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment

Preferred Qualifications:

  • First-author publications at peer-reviewed AI conferences or cog-sci journals
  • Experience conducting original research that can be applied to deep learning research at FAIR

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