Running Theses
This thesis explores the application of diffusion models for the segmentation of brain tumors in 3D MRI images. By leveraging the robust generative capabilities of diffusion models, the research investigates how these models can accurately identify and segment tumor regions in volumetric MRI data. The study focuses on enhancing tumor detection accuracy and addressing challenges ...
This thesis aims to develop a single-cell-centric biomedical foundation model that leverages the capabilities of generative pre-trained transformers to enhance the analysis of single-cell RNA data. The model will address critical tasks in single-cell biology, such as cell-type annotation, perturbation prediction, identification of pathogenic cells, and gene network inference.
This thesis is co-supervised by Sikander Hayat ...
We are looking for a highly motivated master student to work on an innovative project for their master’s thesis as soon as possible. The project involves the implementation and evaluation of a programmable money approach utilizing verifiable credentials and zero knowledge proofs.
A Natural Language Interface for the Semantic Data Lake system (SEDAR) via LLMs
As universities strive to enhance the effectiveness of their lecture exercises, there arises a need for diverse and realistic test user scenarios to evaluate the understandability and usefulness of educational materials. However, in creating such scenarios a number of challenges arise: Real world students can rarely be used for testing, they are likely inexperienced or ...
Fine-tuning pre-trained large language models (LLMs) enhances biomedical text mining. This thesis introduces a tool capable of performing tasks such as Named Entity Recognition (NER), Normalization (NEN), and Knowledge Graph Construction (KGC). A key research question explores how LLMs can address the challenges of named entity recognition, normalization, and relation extraction in biomedical contexts.
This thesis aims to explore the realm of Natural Language Processing (NLP) in the context of automated grading systems, focusing specifically on identifying and analyzing frequent learner mistakes. With the increasing integration of technology in education, automated grading systems have gained prominence, but they often lack nuanced understanding and feedback provision. This research endeavors to ...
Developing a Flexible Interface for Agent-Behaviors inMulti-Agent-System Simulations of Electrical Power Systems
Synopsis operators for a distributed on-the-edge streaming architecture
Detect Flames in industrial Video data using edge devices.
Validating the Reference Architecture Model of International Data Spaces Regarding Vocabulary Sharing Requirements From Life-Science Industry in the Context of Persistent Identifier Systems
