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Postdoctoral Associate, Computer Vision and Machine Learning
  • Job Number: 21556
  • Functional Area: Academic (non-faculty)
  • Department: Media Lab
  • School Area: Architecture & Planning
  • Employment Type: Full-time Temporary (Hybrid)
  • Employment Category: Exempt
  • Visa Sponsorship Available: Yes
  • Schedule:

Job Description

POSTDOCTORAL ASSOCIATEMedia Lab-Camera Culture Group, to join a highly collaborative team of faculty and researchers focusing on computational vision and machine learning in the broad areas of 3D imaging, NERF, high level vision, supervised learning, reinforcement learning, health imaging, and novel computational sensors.  Will perform independent research in areas that include computer vision, machine learning, medical imaging, LiDAR, non-line-of-sight imaging, and 3D imaging modalities (e.g., tomography); publish, present, and disseminate research to the computer vision communities; collaborate with professors from other institutions in their research; work with private, industry, and/or government research sponsors to apply for research grants; help identify projects; mentor graduate students; and supplement a team of researchers to push forward this research. 

A full job description is available at  

Job Requirements

REQUIRED: Ph.D. in computer vision or closely related field; experience with programming in MATLAB or Python; excellent interpersonal, collaboration and written and verbal communication skills; proactive approach to problem solving and time management; and ability to incubate and envision new, innovative research projects and successfully manage their implementation with graduate students. PREFERRED:  a background in or demonstrated experience with computational algorithms (e.g., signal processing, machine learning, and optimization); experience with unique sensors (e.g., stereo, SPAD, time of flight); and an interest in current research activities of the Camera Culture group.  Job #21556

This is a one-year appointment with the possibility of extension based on funding and the course of the research.