Bachelor and Master Theses

We are always looking for motivated students to work on a Bachelor or Master thesis. Below you can find a list of open topics, but please feel free to propose your own topic in any reaearch area of the group.

Open Topics

  • Transfer Learning from Medical Ultrasound to Marine Sonar Image Data

  • Deep Learning-based classification of Phytoplankton in Imaging Flow Cytometry Data

  • Deep Learning-based age estimation of fish from Otoliths

  • Methods for Pseudo-Label Selection for Student-Teacher-based Domain Adapation
    This research focuses on exploring various methods for selecting pseudo-labels in semi-supervised learning scenarios. By evaluating different criteria and strategies for pseudo-label selection, we aim to develop effective techniques for leveraging unlabeled data to improve model performance.

Finished Theses

  • Analyse von Schweinswal-Lauten mit Machine Learning-Methoden

  • Developing a Semi-Supervised Transformer Model for Activity Recognition
    This project focuses on designing and implementing a semi-supervised transformer model for activity recognition tasks. By leveraging both labeled and unlabeled data, the model aims to achieve improved performance and generalization capabilities compared to traditional supervised approaches.

  • Process Mining for Human Activity Sequences in Warehouses
    This project focuses on applying process mining techniques to analyze human activity sequences in warehouse environments. By leveraging process mining algorithms and data collected from sensors, the project aims to uncover processes performed in warehouses. The insights gained from this analysis can inform process optimization strategies, improve workflow efficiency, and enhance overall warehouse operations.

  • Erkennen und Lokalisieren von Anomalien in Wärmebildern von Industriebauteilen mithilfe unüberwachter Lernmethoden

  • A Data-Processing Framework for Time-Series Operational Vessel Data

  • Deep Learning-basierte Analyse von Hämatomen in Hyperspektralbildern

  • Sparse Index Structures in RAG Systems: Efficient Search and Embedding Strategies for Heterogeneous Data Sets