PhD Candidate - Efficient Neural Data Representation | PhD - Effiziente Neuronale Repräsentation von Datensätzen / REF206677I

Job Summary

As a PhD Candidate at the Bosch Center for AI, you will conduct cutting-edge research on deep generative models to improve data efficiency in real-world Bosch systems. Your day-to-day work will involve developing novel algorithms to generate relevant data 'on demand,' allowing networks to learn more effectively by synthesizing targeted training examples. You will work with advanced generative architectures like diffusion models, GANs, and VAEs to transform how datasets are utilized for training and validating downstream neural networks. This role offers the unique opportunity to collaborate with world-class experts in deep learning and computer vision, while actively contributing to the academic community through publications in high-ranking journals and conferences. You will need a strong background in computer science, specifically in deep learning and computer vision, along with proficiency in Python and deep learning frameworks. This position is ideal for a motivated researcher eager to bridge the gap between theoretical generative modeling and practical, industrial-scale AI applications in an international, interdisciplinary environment.