Fraunhofer MEVIS is a world-leading research and development center for computer support in digital medicine. We pursue a patient-centered and clinical workflow oriented approach to solving clinically relevant challenges in image- and data-based diagnosis and therapy.
What you will do
You will be tasked with the critical evaluation and refinement of a visual Machine Learning (ML) toolkit. This toolkit is designed to assist researchers in the navigation and interpretation of complex data sets, guiding them through the process, and fostering the generation of actionable insights. Your role will involve conducting both qualitative and quantitative assessments of the toolkit with users ranging from novices to experts. The data you gathered from these evaluations will then be meticulously analyzed to inform subsequent design recommendations, potentially leading to iterative software enhancements.
The topic is suitable for bachelor or master thesis.
What you bring to the table
What you can expect
We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability.
With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.
Interested? Apply online now. We look forward to getting to know you! Applications are possible in German or English. Please include a cover letter, your CV and your latest transcript of records.
If you have any questions, do not hesitate to contact
Henrik Detjen
henrik.detjen@mevis.fraunhofer.de
Fraunhofer Institute for Digital Medicine MEVIS
Requisition Number: 73917 Application Deadline: