Thesis in Environment Perception for Autonomous Driving starting February 2021

We are constantly looking for highly motivated students to join our autonomous car research team.
One of the problems we try to solve may be described as follows: A pedestrian steps out of a crowd and walks towards the road. Does the pedestrian want to enter the road? To solve this we have to be great at detecting the pedestrian in camera images, tracking her across time without confusing her with other pedestrians in the crowd, understanding non-verbal communication signals and forecasting the intention to enter the road based on the observed path, the environment (traffic lights, distance to curb) and the body posture. We do this by employing state-of-the-art deep learning (e.g. RNNs or VAE), probabilistic methods (e.g. Bayesian Neural Networks) or reinforcement learning.
You can expect plenty of GPU resources, car-loving nerds and the occasional cake (you need to bring one too).
Your responsibilities will include a review of relevant research literature and development of state-of-the-art algorithms that tackle the aforementioned challenges. An expert in the domain will supervise you.

The final thesis selection is made in close consultation with you, the university and us.

    Professional Qualifications:
  • Studies: Computer Science, Artificial Intelligence, Mathematics, Physics, Electrical Engineering or similar
  • Very good communication skills and proficiency in English and German
    Experiences with MS-Office, programming skills in an object-oriented programming language such as Python/ C++, familiarity with Linux
    Familiarity with Tensorflow/ PyTorch, object detection/ tracking/ reinforcement learning/ adversarial neural networks/ time-series prediction
  • Experience in machine learning in general and deep learning/ graph neural networks/ Monte Carlo in particulare
  • Experience in computer Vision, familiarity with ROS (Robot Operating System) are beneficial


    Soft skills:
  • Ability to think ahead
  • Independence

This is a full-time job

It doesnt work completely without formalities. When sending your online application, please attach your CV, certificate of enrollment, current performance record, relevant certificates, proof of mandatory internship and the standard period of study (max. 6 MB).
Citizens of countries outside the European Trade Union please send, if applicable, your residence / work permit.
We particularly welcome online applications from candidates with disabilities or similar impairments in direct response to this job advertisement.
If you have any questions, you can contact the local disability officer once you have submitted your application form, who will gladly assist you in the onward application process: sbv-Sindelfingen@daimler.com
Please understand that we no longer accept paper applications and that there is no right to get your documents returned.

Julian Wiederer, Arij Bouazizi and Phillip Czech from the department will be happy to answer any questions you may have about the position at julian.wiederer@daimler.com, arij.bouazizi@daimler.com or phillip.czech@daimler.com

If you have any questions regarding the application process, please contact HR Services at +49 711/17-99544.

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