PhD - Efficient Neural Representation of Datasets
Aufgaben
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.
Profil - 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
- 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
Kontakt & Wissenswertes
https://www.bosch-ai.com
www.bosch.com/research
Please submit all relevant documents (incl. curriculum vitae, certificates).
You want to work remotely or part-time - we offer great opportunities for mobile working as well as different part-time models or job-sharing. Feel free to contact us.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.
Need support during your application?
Kevin Heiner (Human Resources)
+49 711 811 12223
Need further information about the job?
Jiayi Wang (Functional Department)
+49 711 811 44429
Anna Khoreva (Functional Department)
+49 711 811 46129
Einblicke in unsere Arbeitswelt
Willkommen in Renningen
Unser Forschungscampus in Renningen bildet den internationalen Knotenpunkt unserer Bereiche Forschung und Vorausentwicklung, Cross-Domain Computing Solutions und Bosch Center for Artificial Intelligence. Mitarbeiter aus aller Welt arbeiten daran, Antworten auf die Fragen von Übermorgen zu finden. Damit sich die Ideen unserer Forscher optimal entfalten können, ist der Campus ein Netz der kurzen Wege zwischen Kommunikation und Inspiration, an dem der Kreativität keine Grenzen gesetzt sind. Wollen auch Sie die Zukunft gestalten? Wir freuen uns auf Ihre Neugier und Innovationsfreude.
