PhD - Robust Deep Learning in the Physical World

  • Robert-Bosch-Campus 1, 71272 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

Deep learning (DL) has achieved remarkable results for perceptual tasks within the last decade. However, DL-based perception often lacks sufficient robustness for real-world applications, as exemplified by the existence of adversarial examples and the fragility in face of natural distortions not foreseen during training. Besides, there is growing evidence that DL-based perception works differently than human perception on a fundamental level, e.g. relying overly strong on texture cues and on brittle characteristics of the training data. 

  • Help shape the future: In this PhD, we want to work on fundamentally new methods for DL, for instance new network architectures, new training procedures, or new regularization schemes.
  • Networked communication: The results should be published at the top-tier machine learning venues.

Qualifications

  • Personality: Communcative and team player
  • Working Practice: Independent, motivated to work in an interdisciplinary and international team
  • Experience and Knowledge: With deep learning frameworks (TensorFlow, PyTorch, etc.), basic knowledge of machine learning and deep learning, strong programming skills, in particular Python and strong mathematical background
  • Languages: Very good in English (written and spoken)
  • Education: Excellent degree (Master) in computer science, mathematics or related fields with excellent marks.

Additional Information

The final PhD topic is subject to your university. Duration: 3 years

Please submit all relevant documents (incl. curriculum vitae, certificates).
                                                                                 
Need support during your application?
Kevin Heiner (Human Resources)
+49 711 811 12223

Need further information about the job?
Jan Hendrik Metzen (Functional Department)
+49 711 811 48904

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