PhD – Generative Models for Closed-loop Synthesis

Bosch Group; Renningen; Vor Ort; EUR 45.000 - 75.000; Vollzeit

Zusammenfassung

Ein innovatives Unternehmen sucht einen leidenschaftlichen PhD-Studenten, der an der Schnittstelle von KI und generativen Modellen arbeitet. Diese spannende Rolle bietet die Möglichkeit, neuartige tiefgreifende generative Modelle zu entwickeln, die die Effizienz von Datensätzen in Bosch-Systemen verbessern. Sie werden in einem interdisziplinären Team arbeiten, das sich auf die Entwicklung neuer Ideen konzentriert und darauf abzielt, in führenden Fachzeitschriften zu veröffentlichen. Wenn Sie eine Leidenschaft für Forschung und Entwicklung in einem dynamischen Umfeld haben, ist diese Gelegenheit perfekt für Sie.

Qualifikationen

• Starkes Hintergrundwissen in Deep Learning und Computer Vision.
• Erfahrung mit Deep Learning-Frameworks wie TensorFlow und PyTorch.

Aufgaben

• Entwicklung neuartiger tiefgreifender generativer Modelle zur Verbesserung der Datenquellen.
• Zusammenarbeit mit Experten im Bereich Deep Learning und Computer Vision.

Kenntnisse

Deep Learning

Computer Vision

Python

Generative Modeling

Academic Writing

Ausbildung

Excellent degree in Computer Science

Tools

TensorFlow

PyTorch

Jobbeschreibung

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: We grow together, we enjoy our work, and we inspire each other. Welcome to Bosch.

The Robert Bosch GmbH is looking forward to your application!

Job Description

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.

Qualifications

• 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

Additional Information

https://www.bosch-ai.com
www.bosch.com/research

Please submit all relevant documents (incl. curriculum vitae, certificates).

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?

Sarah Schneck (Human Resources)
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Need further information about the job?

Jiayi Wang (Functional Department)
[phone]
Julia Vinogradska (Functional Department)
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