Success in medical AI isn't just about high accuracy; it's about building models that respect clinical constraints and address real-world needs
Nadia Vohwinkel
Nadia is dedicated to bridging the gap between clinical practice and computational science through interdisciplinary education. Currently, she is developing a framework that brings together students from medicine and machine learning to solve relevant problems in the field of medicine. By fostering a collaborative environment, Nadia focuses on the practical integration of ML solutions while encouraging critical reflection on the technical and ethical challenges inherent to both fields.
Success in medical AI isn't just about high accuracy; it's about building models that respect clinical constraints and address real-world needs
Present Positions And Title
Research Assitant
Research Group
Email Address
Career
| Period | Institution | Role |
|---|---|---|
| Since 2026 | Hertie Institute for AI in Brain Health, University of Tübingen | Research Assistant |
Academic Education
| Year | Degree | Institution | Field of Study |
|---|---|---|---|
| 2026 | MSc | University of Tübingen | Machine Learning |
| 2021 | BSc | University of Tübingen | Mathematics |