Stellenausschreibung152/en
Student Assistant - Sensorfusion and Multi-Object Tracking
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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
Student Assistants / Tutors
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Applied Technical Cognitive Systems
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