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Student Assistant - Sensorfusion and Multi-Object Tracking

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Job Description

A key challenge in automated driving is a robust environment perception. This involves combining information from different sensors and tracking it over time. The use of computationally expensive artificial neural networks under time-critical conditions is a central component in order to make the advances in the field of deep learning practically usable. Within your work as a research assistant you will apply state-of-the-art algorithms for object detection in C++ and make them more robust against false detections using tracking algorithms. The realization with the lowest possible computing time is a core task. The implementation will be done with the middleware ROS and you will have the opportunity to test the algorithms you have developed on a real system.

TASKS You can expect a variety of possible tasks. Among others we are looking for support in the following areas.

  • Implementation of detector and tracking algorithms in C++ with the middleware ROS
  • Use of deep learning in time-critical applications
  • Experimenting with classical filter algorithms

WE OFFER

  • An interdisciplinary research environment with partners from science and industry
  • A constructive collaboration with bright, motivated employees
  • A pleasant working atmosphere
  • Modern hardware
  • Openness to creative ideas

WE EXPECT

  • Ability to implement both state of the art and experimental algorithms
  • Programming experience in C++ necessary
  • Familiarity with ROS and Linux
  • Familiarity with Python, Tensorflow, multi-threading and/or GPU programming advantageous
  • Familiarity with statistical filtering techniques, state estimation, and tracking algorithms advantageous
  • Sound English or German skills
  • High creativity and productivity

REQUIRED DOCUMENTS

  • current transcript of records
  • CV

CONTACT Jens Weber

Job Type

Student Assistants / Tutors

Link PDF

No information available

Contact Person

Jens Weber

Research Group

Applied Technical Cognitive Systems

Closing Date for Applications

No information available