PhD Generative Models for Closed-loop Synthesis
PhD Generative Models for Closed-loop Synthesis
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Job Description
We are conducting cuttingedge 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 realworld scenarios for AI training and validation.
The development of AI models is often an iterative process that requires increasingly large datasets to address longtail cases that are not represented in existing data. However collecting data from the real world can be timeconsuming 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 realworld facilitating closedloop 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 toptier 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 peerreviewed 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 :
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)
Need further information about the job
Jiayi Wang (Functional Department)
49 9
Julia Vinogradska (Functional Department)
49 7
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
