Probabilistic Generative AI Using Gaussian Mixture
- Forschungsthema/Bereich
- Artificial Intelligence
- Typ der Abschlussarbeit
- Bachelor / Master
- Startzeitpunkt
- -
- Bewerbungsschluss
- 30.06.2028
- Dauer der Arbeit
- 4 months(BSc) - 6 months(MSc)
Beschreibung
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.Voraussetzung
- Voraussetzungen an Studierende
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- There are no hard constraints but the more programming and math you know the more you can have fun while doing the project.
- Studiengangsbereiche
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- Ingenieurwissenschaften
Informatik
- Ingenieurwissenschaften
Betreuung
- Titel, Vorname, Name
- Ali Darijani
- Organisationseinheit
- Computer Science(IAR/IES)
- E-Mail Adresse
- ali.darijani@iosb.fraunhofer.de
- Link zur eigenen Homepage/Personenseite
- Website
Bewerbung per E-Mail
- Bewerbungsunterlagen
-
E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an ali.darijani@iosb.fraunhofer.de
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