Research Scientist, Neural Interfaces (PhD)

Facebook Reality Labs is seeking a Research Scientist to help us unleash human potential by eliminating the bottlenecks between intent and action. To achieve this, we’re building a practical neural interface drawing on the rich motor neuron signals that can be measured non-invasively with neuron-level resolution. Our research lies at the intersection of computational neuroscience, machine learning, signal processing, statistics, biophysics, motor learning, perceptual psychophysics, and human-computer interaction.

We’re looking for people who want to shape the future of this technology and are excited about joining our collaborative research team that has grown out of the acquisition of CTRL-labs.


Research Scientist, Neural Interfaces (PhD) Responsibilities

  • Plan and execute cutting-edge applied research to advance neural interface capabilities. Collaborate with engineering and HCI teams to translate fundamental scientific knowledge into new technology.
  • Use quantitative research methods to define, iterate upon and advance key areas of our research agenda.


Minimum Qualifications

  • Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.
  • Currently has, or is in the process of obtaining, a PhD in the field of computational neuroscience, systems neuroscience, machine learning, physics, computer science, electrical engineering, or related fields.
  • Research-oriented software engineering skills, including fluency with libraries for scientific computing (e.g. SciPy ecosystem) and machine learning (e.g. Scikit-learn, PyTorch, TensorFlow).
  • Proficiency with quantitative methods (mathematics, statistics) and experience learning new technical knowledge and skills rapidly.
  • 3+ years of experience working autonomously to design, execute, interpret, and present research studies.


Preferred Qualifications

  • Experience with cross-domain and cross-culture collaboration.
  • Experience in signal processing and familiarity with real-time signals.
  • Experience with large scale cluster computing for machine learning modelling.

Please click here to apply:

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