UserAccount Thesis Bridging the Sim2Real Gap for Sensor Data in an Industrial Context via Generative Models 36754
UserAccount Thesis Bridging the Sim2Real Gap for Sensor Data in an Industrial Context via Generative Models 36754
Employer Active
Job Alert
You will be updated with latest job alerts via email
Share
Job Alert
You will be updated with latest job alerts via email
Job Description
Winter Semester 2025/26 limited to 56 months
Machine learning (ML) models are increasingly hungry for data. In industrial contexts highquality labeled data is a scarce and costly resource. Synthetic data enables ML model training algorithm evaluation or sensor design without the burden of exhaustive data collection. However generating realistic data is difficult because not all attributes of sensors or the environment are captured (i.e. simulationrealitygap). Recently generative models have emerged as a promising avenue potentially helping in generating synthetic data that closely mimics realworld scenarios. The goal in this thesis will be to evaluate the potential of generative models to bridge the gap between realistic and synthetic sensor data in an industrial context.
YOUR TASKS:
- You perform a literature review on generating realistic sensor data via generative models
- You familiarize yourself with sensor simulation in Nvidia Omniverse
- You explore the applicability of 12 generative approaches on a specific industrial use case
- You evaluate the performance of considered approaches on a downstream task
YOUR PROFILE:
- You are familiar with generative models such as diffusion models or GANs
- You may bring experience in machine learning domains such as computer vision audio processing or NLP demonstrating expertise in system training evaluation and understanding of these systems
- You might have designed your own games or tinkered with the industrial Metaverse
- You are passionate about exploring the applicability of Generative AI for a specific industrial use case
YOUR APPLICATION:
- We are looking forward to your online application
- Sarah Disch
- JobID36754
- All applications will be treated confidentially
*At SICK we see people not gender.
We put great emphasis on diversity reject discrimination and do not think in categories such as gender ethnicity religion disability age or sexual identity.
Stichworte:Intern Praktikum Praktikant
Employment Type
Intern
