DBIS

This master’s thesis builds on the Psychomotor Feedback Engine (PFE) and the IMPECT framework [1], [2], [3], aiming to improve the graphical user interface that teachers use to implement rules and feedback elements in psychomotor learning.

This thesis aims to address those usability issues by redesigning the teacher interface and integrating the IMPECT framework to introduce a more comprehensive feedback mechanism. The IMPECT framework introduces two core concepts: “feedback cards” and exercise stages. Feedback cards provide structured guidance on how exercises should be performed, while exercise stages break down training into a series of sequential tasks. Together, these elements can create a more dynamic and responsive feedback system, offering learners timely, actionable feedback during their training sessions.

This thesis focuses on developing a Transcriptome-Language Model (TLM) to effectively bridge the modality gap between transcriptomic data and natural language text. You will explore advanced models for transcriptomic data representation, and cross-modal learning techniques aligning transcriptomic and textual modalities. This model will be evaluated in tasks such as zero-shot cell property classification and text generation.

This thesis is co-supervised by Sikander Hayat and Rafael Kramann, Department of Medicine II, University Hospital Aachen.

Please send your application to Yongli Mou, M.Sc. (mou@dbis.rwth-aachen.de) and CC. Dr. Sikander Hayat (shayat@ukaachen.de)

This project aims to develop a comprehensive knowledge graph that represents German law documents, including cases and statutes. By creating an ontology tailored to the legal domain and leveraging automated annotation techniques, the project will transform unstructured legal text into structured data that can be queried. This knowledge graph will support legal research, enhance information retrieval, and enable semantic analysis of German legal documents.

This thesis investigates the application of federated learning (FL) to the Personal Health Train (PHT) paradigm, exploring how FL can be better adapted to improve privacy-preserving data analysis in healthcare. The research examines how PHT can facilitate secure, distributed machine learning on sensitive medical data across different institutions, while ensuring data privacy and compliance with regulatory standards.

This research investigates hallucination in vision-language models, focusing on the role of the attention mechanism in contributing to and potentially mitigating hallucinations. The work explores how attention layers influence the integration of visual and textual information and identifies techniques for reducing the generation of inaccurate or irrelevant outputs. A critical research question is understanding how attention mechanisms can be adjusted or improved to decrease hallucination in vision-language models, thus enhancing reliability in applications like image captioning and visual question answering.

This thesis focuses on designing an Agentic Graph Retrieval-Augmented Generation (RAG) system specifically for question answering in oral maxillofacial surgery (OMS) guidelines. By leveraging graph-based knowledge representation and advanced language models, the system aims to improve accuracy and efficiency in accessing and interpreting surgical guidelines. Key research areas include the integration of graph databases, ontology-based retrieval, and natural language processing to facilitate precise, contextually relevant responses to complex clinical queries.

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 associated with 3D medical image segmentation, such as complex tumor shapes and varying intensities.

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 and Rafael Kramann, Department of Medicine II, University Hospital Aachen.

Please send your application to Yongli Mou, M.Sc. (mou@dbis.rwth-aachen.de) and CC. Dr. Sikander Hayat (shayat@ukaachen.de)