Deep Learning Researcher - Generative Models for Automated Driving

  • Robert-Bosch-Campus 1, Renningen, Germany
  • Full-time
  • Legal Entity: Robert Bosch GmbH

Company Description

Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology – with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch.

The Robert Bosch GmbH is looking forward to your application!

Job Description

  • Research on generative models for environmental modeling and decision making; learning behavior models from demonstrations with accurate long-term predictions

  • Development and implementation of algorithms for learning to imitate single- and multi-agent behavior from demonstrations

  • Original research, theoretical investigations, publications at top Machine Learning conferences and journals

  • Close contact to the scientific community in Machine Learning, scouting and assessment of new approaches

  • Technical discussions and creation of new ideas within the existing Machine Learning research team 

  • Supervision of Master and PhD students

Qualifications

  • PhD in Machine Learning (preferably related to generative modeling, GANs) with excellent publication record

  • Proven programming skills, in particular Python and DL-frameworks (PyTorch, TensorFlow, …)

  • Experience in development and implementation of state-of-the-art DL technologies

  • Broad knowledge of machine learning algorithms and principles as well as probability theory

  • Experience with real-world applications

  • Strong teamplayer, motivation and ability to define personal research roadmap  

  • Strong English skills, motivation to work in an interdisciplinary and international team

Additional Information

Need support during your application?
Michael Streitmayer (HR Department)
+49 711 811 33306

Need further information about the job?
Michael Hanselmann (Business Department)
+49 711 811 49420

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