Student (f/m/d) for master thesis "Development of a User-Friendly Research Software Prototype for Liver MRI-Based Prediction Pipeline"
Starting Date: as soon as possible
Supervising Institutions:
- Department of Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen (Prof. Dr. Carolin Schneider)
Background
Recent advances in medical imaging and artificial intelligence have enabled the development of research pipelines that extract quantitative information from liver MRI data and use these features for downstream prediction tasks, such as the prediction of genetic variants. In our group, we have established an MRI-based workflow that includes key steps such as water-fat separation, liver segmentation, and prediction.
However, such pipelines are often developed as research code and remain difficult to use for non-technical researchers or clinicians. Limited usability, insufficient modularity, and the lack of deployment-ready interfaces restrict their broader application in translational research settings. Therefore, there is a strong need to transform existing research pipelines into more robust, reproducible, and user-friendly software tools.
Aim of the project
The aim of this project is to translate an existing liver MRI analysis pipeline into a modular and user-friendly research software prototype that can be used by non-technical users. The software should integrate the main processing steps of the current workflow, from MRI input to automated quantitative analysis and common downstream prediction tasks (e.g., regression, classification, segmentation). In addition, the project will explore how such a tool could be deployed in a secure research environment such as UK Biobank researcher analysis platform (RAP).
Objectives
The student will work on the following tasks:
- Analysis and restructuring of the existing pipeline
Review the current MRI-based workflow, understand and identify its major components, dependencies, inputs, and outputs. - Software-oriented modularization
Refactor the pipeline into a clearer and more maintainable structure, with separate modules for data input, water-fat separation, liver segmentation, feature generation, and prediction. - Development of a user-friendly interface
Design and implement a simple interface that allows non-technical users to run the workflow with minimal manual intervention. Depending on feasibility, this may take the form of a graphical interface, a guided command-line workflow, or a lightweight web-based front end. - Standardization of outputs and documentation
Define standardized outputs, including intermediate quality control information and final prediction results, and provide concise user documentation. - Prototype deployment and RAP feasibility assessment
Investigate how the software prototype can be deployed in a secure research environment such as RAP, including technical requirements, limitations, and possible implementation strategies. - Evaluation of the prototype
Perform an initial evaluation of the software with respect to reproducibility, usability, runtime behavior, and robustness in realistic usage scenarios.
Research component
In addition to software development, the project should include a scientific evaluation of the developed prototype. Possible research questions include:
- How reproducible are the pipeline outputs across repeated runs and different execution settings?
- Which parts of the workflow represent the main bottlenecks for usability and accessibility by non-technical users?
- What are the practical challenges in translating a research-grade MRI analysis pipeline into a deployable software prototype for use in secure data environments?
This component is intended to ensure that the project is not only an implementation exercise, but also contributes methodological insight into research software development for medical AI workflows.
Expected Outcomes
The expected outcomes of the project include:
- A modular prototype software for liver MRI-based analysis and prediction
- A user-oriented workflow that can be applied by researchers without strong programming experience
- Documentation of the software structure, inputs, and outputs
- An initial assessment of deployment feasibility in RAP
- A written evaluation of reproducibility, usability, and technical limitations
Preferred Skills
This project is suitable for a Master’s student with an interest in medical AI, biomedical data science, or research software engineering. Useful prior experience includes:
- Python programming
- Basic machine learning and deep learning knowledge
- Interest in medical imaging or biomedical data analysis
- Familiarity with software design, workflow automation, or interface development
Prior experience with MRI processing is helpful but not strictly required, provided the student is motivated to learn.
Application:
Please submit your application through our application portal, quoting GB-P-55381. The application deadline is April 29th 2026.
Contact:
For more detailed information please contact Prof. Dr. med. Carolin Victoria Schneider:
E-Mail: cschneider@ukaachen.de
Yazhou Chen, M.Sc.:
E-Mail: yachen@ukaachen.de
We look forward to receiving your application!
This position is not gender specific.
The RWTH Aachen University Hospital promotes equal opportunities and diversity. Applications from women are expressly encouraged and if the applicant is suitable qualified, they will be given priority in accordance with the LGG. If suitably qualified, people with a registered disability will also receive priority.
Weekly hours are negotiable.
You should preferably use our digital application portal at www.karriere.ukaachen.de for your application. There you have the option of securing your documents in the electronic application folder to prevent unauthorized access. Applications that reach us by email to: bewerbung@ukaachen.de (this transmission path cannot be as effectively secured) will be transferred to the aforementioned portal and any accompanying documents will be disposed of in accordance with data protection regulations immediately after transfer. After the retention period has expired, the data in the portal will also be deleted. If you do not agree to a transfer to the Application portal your application cannot be considered.