MIT – Postdoc in Biologically Inspired Deep Learning

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Postdoctoral Associate-Biologically Inspired Deep Learning

• Job Number: 21093
• Functional Area: Research – Scientific
• Department: Brain & Cognitive Sciences
• School Area: Science
• Employment Type: Full-Time Temporary
• Employment Category: Exempt
• Visa Sponsorship Available: Yes
• Schedule:

Information on MIT’s COVID-19 vaccination requirement can be found at the bottom of this posting

POSTDOCTORAL ASSOCIATE-BIOLOGICALLY INSPIRED DEEP LEARNING, Brain and Cognitive Sciences (BCS), to join a multidisciplinary project that aims to translate discoveries from human visual perception into machine deployable strategies in the domain of face and body recognition in low-quality imagery. This project is funded by IARPA (Intelligence Advanced Research Projects Activity) and will take place at the Sinha Laboratory at MIT under the supervision of Professor Pawan Sinha and Dr. Xavier Boix.  There is a big gap between machine and biological learning in terms of the ability to recognize people in a number of degraded conditions (e.g., at a distance and in novel viewpoints and illumination conditions). Will develop a computationally-grounded understanding of the principles that allow for the human robust recognition and translate this understanding into algorithms, and collaborate and interact with other team members who experiment with human subjects to gain new insights about the strategies used by humans.  

Job Requirements

REQUIRED:  Ph.D. in computer science, linguistics, cognitive science, or related field by the start date; expertise in computer vision, deep learning, and computational neuroscience.  Job #21093

Questions or inquiries about the position may be directed to Xavier Boix at xboix@mit.edu.

Application material should include a CV, cover letter, research statement, and contact information for three potential letter writers (in a single pdf).

The target start date is as soon as possible.

This is a one-year position with the possibility of extension based on funding.