PhD – Generative Models for Closed-loop Synthesis

We are conducting cutting-edge research on advanced generative models aimed at enhancing data efficiency in Bosch systems. We are seeking a PhD student who is passionate about exploring innovative applications of generative models (such as diffusion and autoregressive models) to simulate real-world scenarios for AI training and validation. 

The development of AI models is often an iterative process that requires increasingly large datasets to address long-tail cases that are not represented in existing data. However, collecting data from the real world can be time-consuming and expensive, hindering the automation of the data loop. The objective of this thesis is to create new methodologies that enable generative models to substitute for the real-world, facilitating closed-loop interactions. This may involve designing novel control mechanisms to efficiently sample the required data and respond to interactions.

As a member of our team, you will: 

• Develop novel deep generative models (e.g., diffusion models) as data sources to enhance the training and validation of downstream models.
• Collaborate with experts in deep learning and computer vision at the Bosch Center for AI to brainstorm and develop new ideas.
• Aim for publications in top-tier journals and conferences.

Function: Research
Experience Level: Entry Level