Theses & Projects
Shape the future with your own research! We offer "wissenschaftliche Projekte" (WissPro's) as well as Bachelor, Master and PhD Theses with close supervision and exciting topics at the intersection of innovation and application in following areas:
Medical Data Science
AI Ethics in Medicine
Research Data Management
Medical Informatics Technologies
Innovation and Entrepreneurship
If you are interested to pursue a thesis in one of those areas, please reach out and, if possible, include your personal experience and interests.
Below you find a list of currently offered theses. If you do not find anything fitting, please still reach out.
Open WissPro's
Representation of a patient in precision medicine and digital twins
This project explores how patients are represented in precision medicine and digital twin approaches, and how different representation strategies affect their usefulness for specific clinical tasks. In precision oncology, for example, only a small subset of a patient’s genes may be relevant for selecting an effective cancer treatment, while other information may be less important for that decision. Choosing the right way to represent the patient—what data to include, how detailed it should be, and how it is organized—can strongly influence clinical insights and outcomes. The goal of this project is to analyze and compare different ways of representing patients, from simple, task-focused summaries to more complex, dynamic models. By doing so, it aims to identify strategies for building patient representations that are both relevant and efficient for a given task, while remaining interpretable and clinically meaningful.
Practical cases of DiGA certifications for software products and services
DiGAs (‘Digitale Gesundheitsanwendungen’) offer a recently introduced way to bring healthcare and medical innovations closer to the market. The focus of this project is to provide practical information on the certification process and also provide example cases.
Bias detection and mitigation in AI models
How objective or how biased are AI-based predictors? Bias detection and mitigation in AI focuses on identifying and reducing unfairness in AI systems. Bias can emerge from data, algorithms, or model assumptions, leading to unequal treatment across groups. Detecting these biases and applying mitigation strategies ensures AI is not only accurate but also fair, transparent, and socially responsible.
Open Bachelors Theses
Benchmarking of domain adaptation methods in medical AI
AI systems in medicine often lose performance when models are transferred to new hospitals, scanners or patient groups. Domain adaptation promises to improve generalization, yet choosing the right method for a given clinical setting remains difficult. This thesis focuses on developing a robust benchmarking pipeline to systematically evaluate domain adaptation in medical AI. The student will assemble suitable datasets representing domain shift, implement a curated set of adaptation strategies and core model architectures, and define relevant evaluation metrics including performance, robustness and fairness across subpopulations. The pipeline will be used to identify strengths, weaknesses and practical deployment considerations of each approach. The final outcome will be an open, reproducible toolkit that helps researchers and clinicians select reliable adaptation methods for real-world healthcare applications.
Fair, unbiased prediction of Alzheimer’s Disease using a multimodal dataset
Artificial intelligence (AI) is becoming increasingly prevalent in the healthcare sector. Medical imaging in particular benefits from AI in the field of computer-aided diagnosis and visualization of medical images, as radiology is particularly well suited to using AI techniques due to its data-driven nature. Likewise, with the increasing application of AI, its biases have become visible concerning e.g., religion, race and ethnicity (biological and socio-cultural meaning), as well as sex and gender. Therefore, methods for bias detection and bias mitigation have been developed for bias identification in existing AI models as well as bias prevention during model development. Using the gender gap in the diagnosis of Alzheimer's disease as an example, the current project focuses on predicting Alzheimer's disease using 3D MRI image data as well as 2D clinical and genetic data. In addition, a fair and unbiased predictor is being developed using methods for detecting and mitigating bias.
Federated Breast Cancer Detection with Flower
Artificial intelligence (AI) models can provide clinical decision support, but require vast and representative patient data to perform well. However, relevant patient data collected by healthcare institutions, such as hospitals, can not be easily shared for computations due to privacy risks. One approach to enable AI model training on distributed medical data is federated learning, which enables privacy-preserving training of AI models. The goal of this project is to develop a federated learning approach for breast cancer detection using the established Breast Cancer Wisconsin dataset and the Flower framework. The dataset will be divided between two simulated hospital clients to mimic real-world distributed medical data. The simulated clients should collaboratively train a global model through iterative federated learning rounds, without sharing their local data. Different machine learning models and aggregation strategies are tested and compared and results will be documented and presented by the student.
From Graph Structure to Vector Space: GNN-Based Knowledge Graph Embeddings
Knowledge graphs enable the structured representation of complex relational information. By explicitly modeling entities and their relationships, knowledge graphs enable reasoning on rich representations beyond isolated data points. However, to effectively use knowledge graphs in traditional downstream machine learning tasks, they must be transformed into vector representations that preserve both structural and semantic properties. Graph Neural Networks (GNNs) offer a powerful and flexible way for learning such embeddings directly from graph-structured data, while outperforming traditional knowledge graph embedding methods such as graph2vec. In this project, the student will investigate how different GNN architectures, message-passing strategies, and hyperparameter settings influence the quality of knowledge graph embeddings. The goal is to understand how embedding models can be adapted to the requirements of a specific knowledge graph and task, and how design choices impact performance and interpretability.
Multi-Objective Optimization for RAG-based LLM
This project explores how conversational information retrieval systems can intelligently balance multiple objectives such as efficiency, correctness, and completeness. The goal is to design mechanisms that decide whether a system should retrieve new information, reuse previous conversation history, or treat a user’s message as the start of a new topic. Students will develop and evaluate optimization strategies that aim to provide the user with the most relevant and accurate responses while minimizing latency and computational cost. The project combines principles from information retrieval, dialogue systems, and multi-objective optimization to enhance both user experience and resource efficiency.
Open Master Theses
From Graph Structure to Vector Space: GNN-Based Knowledge Graph Embeddings
Knowledge graphs enable the structured representation of complex relational information. By explicitly modeling entities and their relationships, knowledge graphs enable reasoning on rich representations beyond isolated data points. However, to effectively use knowledge graphs in traditional downstream machine learning tasks, they must be transformed into vector representations that preserve both structural and semantic properties. Graph Neural Networks (GNNs) offer a powerful and flexible way for learning such embeddings directly from graph-structured data, while outperforming traditional knowledge graph embedding methods such as graph2vec. In this project, the student will investigate how different GNN architectures, message-passing strategies, and hyperparameter settings influence the quality of knowledge graph embeddings. The goal is to understand how embedding models can be adapted to the requirements of a specific knowledge graph and task, and how design choices impact performance and interpretability.
Open PhD Theses
No entries found
Does this sound interesting?
We look forward to receiving your application! Simply send an email with your CV and a brief description of your relevant experience and interests to the contact person for the respective project.
Don’t see an officially advertised thesis?
No problem! We’re always working on exciting projects and are happy to discuss individual thesis ideas supporting these projects - just get in touch and we will surely find something fitting.
Ongoing Theses & Projects
In addition to our open thesis topics, our students are already exploring a variety of exciting research questions. These ongoing projects highlight innovative approaches and the diverse interests within BI-K.
PhD Theses
The thesis contributes to a broader project to systemically identify outcome predictors for deep brain stimulation (DBS) in the German registry of paediatric DBS in patients with childhood-onset dystonia (GEPESTIM) based on structured records and MRI data. The proposed goals is to define the success of DBS in children with dystonia and creating a reproducible and automated cohort characterization to enable further research, meaningful conclusions and comparisons for GEPESTIM.
This medical doctoral project evaluates whether publicly accessible Large Language Models (LLMs) can provide medically accurate and empathetic answers to questions from patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Using realistic patient personas, disease-related questions are posed to different LLMs and evaluated by nephrology experts and patients with ADPKD. The study aims to assess the potential and limitations of LLMs as patient information tools and to support the development of guidance for responsible and unbiased LLM use in healthcare.
Master Theses
Completed Theses
From its very beginning, the BI-K was invested in supporting the next generation of researchers. Below you can see theses which have already been completed.
PhD Theses
Master Theses
[...] Medical Data Integration Centers (MeDICs) like the MeDIC Cologne are currently established to ease the Data Acess and Integration (DAI) process for researchers making medical data available for research all over Germany. However, these MeDICs still face technical, legal, ethical, and organizational problems. This thesis aimed at supporting the definition of concrete DAI processes by proposing a DAI workflow that serves as a basis for discussing concrete DAI project designs as well as optimizing DAI processes at university hospitals[...]