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Master Thesis - Safety verification of Deep Neural Networks for highly automated driving (all genders welcome) AUDI AG

To be filled: 01.04.2019

Work Environment

The newly established Dependable AI Systems group at AUDI AG is pursuing cutting edge multidisciplinary research in dependability analysis and safety verification of learning-based AI systems with a particular focus on providing methods and tools for the development of Audi next-generation highly automated driving. Your involvement in the predevelopment of automated driving at AUDI AG enables you to gain experience in this rapidly growing field and to participate in the development of conditionally and highly automated driving functions in an environment that is both scientifically oriented and practical at the same time. 

Deep neural networks have been frequently applied in various domains such as image classification; however, they can surprisingly be unstable with respect to adversarial attacks or perturbations. This has led to safety concerns of deploying deep neural networks to safety-critical systems such as autonomous driving. To account for the latter and as a part of your thesis, you will develop new concepts and algorithms to ensure robustness and safety of neural networks.

Job Purpose/Role

  • Learning and mastering the relevant state of the art 
  • Concept development and prototypical implementation of algorithms 
  • Evaluation of the methods based on measured data 

    The exact topic is defined in collaboration with the supervising university.

Key Requirements/Skills/Experience

  • Course of studies in a technical area (e. g. computer science, mathematics or comparable) 
  • Excellent academic track record 
  • Domain knowledge in deep learning or verification 
  • Good programming skills in Python or C++ 
  • The ability to work autonomously in an international environment, creativity, team spirit and a pro-active attitude are imperative

Additional information​​​​​​​

This position is available at  AUDI AG in Ingolstadt.

Just for reasons of easier readability, only the grammatical male form is used in person names. This always means people of every gender identity. 

We embrace diversity, actively encourage inclusion and create an environment that fosters each employee’s individuality in the interests of the company.

Reference code: I-A-58294

Details

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