PhD – Efficient Neural Representation of Datasets

Bosch Gruppe


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The Robert Bosch GmbH is looking forward to your application!

Employment type: Limited
Working hours: Full-Time
Joblocation: Renningen

We conduct research on state-of-the-art deep generative models that are used to enable real-world Bosch systems to be data-efficient. We are looking for a PhD student who is interested in researching creative applications of generative models (e.g. stable diffusion) as a controllable dataset representation for training and validating networks for downstream tasks.

Not all data points in a dataset are equally important for the performance of a neural network. As training progresses, loss on some data points might become uninformative since the network already learned what it can from it. As such, it can be advantageous to observe the network training to serve it the right type of data at the right time. However, simply selecting data from a fixed dataset could be problematic when no image with the precise mix of attributes exists. The goal of this PhD project is to develop new learning algorithms for generating relevent data "on demand" in response to the need of the target network. This includes improving training efficiency by synthesizing the most relevant data, enforcing desired invariance by creating example, etc.

  • As part of our team, you will develop novel approaches to adapt deep generative models (e.g. diffusion models, GANs, VAEs) as data sources to better train and validate downstream models.
  • Furthermore, you exploit the controllability and knowledge present in generative base models to move past seeing datasets as just a collection of images.
  • You discuss and develop new ideas within the deep learning and computer vision experts at Bosch Center for AI.
  • Last but not least, publications in top-tier journals and at conferences follow.

  • Education: excellent degree in Computer Science, or related field with focus on Computer Vision and Deep Learning
  • Experience and Knowledge: strong background in deep learning and computer vision, experience with deep learning frameworks (TensorFlow, PyTorch, etc.), strong programming skills, in particular Python, knowledge and experience in deep generative modeling as well as foundation models are a plus, experience with publication of peer-reviewed research papers is beneficial
  • Enthusiasm: motivation to work in an interdisciplinary and international team
  • Languages: very good English skills and academic writing skills

  • Work-life balance: Flexible working in terms of time, place and working model.
  • Health & Sport: Wide range of health and sports activities.
  • Childcare: Intermediary service for childcare services.
  • Employee discounts: Discounts for employees.
  • Room for creativity: Space for creative work.
  • In-house social counseling and care services: Social counselling and intermediary service for care services.

The recruitment contact or superior will be happy to provide information about the individual benefit plan.

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