Probabilistic Generative AI Using Gaussian Mixture
- Research topic/area
- Artificial Intelligence
- Type of thesis
- Bachelor / Master
- Start time
- -
- Application deadline
- 30.06.2028
- Duration of the thesis
- 4 months(BSc) - 6 months(MSc)
Description
Probabilistic Generative AI models uncertainty, generate realistic data, and work well with limited or noisy datasets. They are interpretable, support unsupervised learning, and enable principled reasoning. Their flexibility, robustness, and theoretical grounding make them valuable for data augmentation, simulation, anomaly detection, and tasks requiring uncertainty estimation or structured data modeling. Priors are essential in probabilistic models as they encode prior beliefs and guide learning, especially with limited data. Gaussian mixtures are particularly interesting as priors because they model complex, multi-modal distributions effectively. This flexibility allows capturing diverse patterns in real data, making them ideal for generative tasks and Bayesian inference.Requirement
- Requirements for students
-
- There are no hard constraints but the more programming and math you know the more you can have fun while doing the project.
- Faculty departments
-
- Engineering sciences
Informatics
- Engineering sciences
Supervision
- Title, first name, last name
- Ali Darijani
- Organizational unit
- Computer Science(IAR/IES)
- Email address
- ali.darijani@iosb.fraunhofer.de
- Link to personal homepage/personal page
- Website
Application via email
- Application documents
-
E-Mail Address for application
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an ali.darijani@iosb.fraunhofer.de
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