We are looking for a motivated student to support our AI research on-site in Freiburg. You will explore and improve denoising diffusion models for multimodal anomaly detection focusing on modern transformer architectures for image encoding and denoising. You will experiment with real-life industrial datasets as well as large publicly available benchmarks.  

What are your tasks? 

• Research and evaluate diffusion models for anomaly detection
• Work with transformer-based encoders (e.g., JIT) and diffusion transformers (DiT) denoisers
• Perform experiments on datasets such as KAPUTT
• Investigate recent advances (flow matching, sampling strategies, feature fusion)
• Document and present your results 

This is what makes you special 

• You are studying Computer Science, Artificial Intelligence, Data Science, Applied Mathematics or related.
• You are curious, proactive in solving complex research problems and enjoy experimenting with new ideas.
• You possess strong analytical thinking and enthusiasm for modern machine learning research.
• You already have some experience with generative models (e.g., diffusion models, VAEs, GANs) or anomaly detection methods.
• You have some initial knowledge Python, PyTorch or TensorFlow, Linux environments, GPUaccelerated trainings, and common ML tooling (Git, MLflow, etc.).
• You speak fluent English. 

What you can expect from us 

• Professional and personal support from experienced supervisors
• The opportunity to work independently on challenging projects
• A state-of-the-art working environment in an internationally active family business
• "State of the art" equipment to get off to a flying start
• Home office in consultation  
• Individual company and ceam events
• Deutschlandticket is included!