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Perception for Autonomous Driving Research Intern

Company Description

Bosch Center for Artificial Intelligence in Israel is a unique place that researches and implements cutting edge technologies for manufacturingautomotiverobotics, and industrial applications.

Job Description

Robert Bosch GmbH is a major player in the global automotive market, and is developing perception systems powering autonomous driving functionality.  One sensor of particular interest is the automotive radar, which is a cheap modality compared to Lidar and camera, yet with a long sensing range (up to a few hundred meters), providing (radial) velocity information, and exceptionally resilient to weather and visibility conditions. However, the processing of radar measurements can be involved due to sensor specifics (measurement representation, trade-off between sparsity and data rate, multipath reflections). Modern automotive radar is a relatively recent and under explored technology, with vast potential for impactful innovation.

We are looking for Graduates to do research on AI-driven radar perception, in particular – one of the following topics:

  • Radar semantic perception (3D object detection, segmentation)
  • Radar simulation from RGB

If these topics interest you, or you have your own ideas on related topics, then Bosch Israel is looking forward for your application!

Basic information and requirements

  • Duration: 5-12 months
  • Create something new: As a research intern scientist you will conduct excellent research on a real-world project. The results will be published at the leading AI venues and transferred for implementation in Bosch products.
  • Strong SW engineering capabilities: High proficiency in Python, Deep Learning frameworks, and CV methods in general.


  • Currently pursuing (or completed) a PhD or MSc in CS EE IE ME or related departments
  • At least one submitted publication in a relevant international academic conference or journal
  • Advantages (depending on the desired direction):
    • FMCW Radar / signal processing background
    • computer vision or geometry processing background, hands-on experience with FMs
    • Experience with automotive datasets, 3D object detection
    • Experience with generative models (images / pointclouds)


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