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PhD Researcher in Deep Learning for Data Auditing

vor 2 Monaten


Renningen, Baden-Württemberg, Deutschland Robert Bosch GmbH Vollzeit
PhD Researcher in Deep Learning for Data Auditing

We are seeking a highly motivated PhD researcher to join our team and contribute to the development of novel deep learning methods for improving and auditing data at scale.

The successful candidate will work on cutting-edge research projects, leveraging foundation models to enable data analysis at scale, provide insights into models' generalization capabilities, and aid performance estimation in new domains.

The goal of this position is to develop novel and automated data auditing methods, leveraging deep generative models and foundation models. The researcher will collaborate with Machine Learning and Computer Vision experts, publish in top-tier journals and conferences, and discuss and develop new ideas within the Deep Learning research team at Bosch Corporate Research.

Key Responsibilities:

  • Develop novel deep learning methods for improving and auditing data at scale
  • Leverage foundation models to enable data analysis at scale and provide insights into models' generalization capabilities
  • Aid performance estimation in new domains using deep generative models and foundation models
  • Collaborate with Machine Learning and Computer Vision experts to advance data filtering and annotation strategies
  • Publish research findings in top-tier journals and conferences

Requirements:

  • Excellent degree in Computer Science or related field with focus on Machine Learning/Deep Learning
  • Strong background and experience in Deep Learning, strong programming skills, in particular Python, knowledge in Deep Learning frameworks (TensorFlow, PyTorch, etc.)
  • Knowledge and experience in foundation models, generative models, and out-of-distribution detection are a plus
  • Motivation to work in an interdisciplinary and international team
  • Fluent in English (written and spoken)

Additional Information:

The final PhD topic is subject to your university. We welcome all applications, regardless of gender, age, disability, religion, ethnic origin, or sexual identity. If you need support during your application, please contact Sarah Schneck (Human Resources). For further information about the job, please contact Dan Zhang (Functional Department).