Summary
The Hertie Institute for AI in Brain Health at the Faculty of Medicine, Tübingen, is offering a PhD scholarship focused on developing trustworthy AI algorithms for automated glioma diagnostics. This position combines cutting-edge research with academic growth in a supportive international environment.
PhD in Trustworthy AI for Medical Image Analysis, Hertie Institute for AI, Germany
Designation
PhD Researcher (F/M/D)
Location
Eberhard Karls University, Tübingen, Germany
Research Area
- Medical Image Analysis
- AI in Oncology
- Deep Learning for Automated Diagnostics
Eligibility/Qualification
| Criteria | Details |
|---|---|
| Degree | Master’s degree (or equivalent) in a quantitative field (e.g., Computer Science, Physics, Data Science, Biomedical Engineering, or Neuroscience) |
| Programming Skills | Proficiency in Python or R, and experience with machine learning/deep learning applications |
| Language Proficiency | Functional command of English |
| Research Interest | Interest in oncology or neuro-pathology (prior medical knowledge not required) |
| Personal Attributes | Curiosity, independence in scientific thinking, and openness to multidisciplinary collaboration |
Job Description
Key responsibilities include:
- Model Development: Train and refine deep learning models for automated glioma diagnostics focusing on interpretability.
- Research & Discovery: Develop novel methods to quantify tumor characteristics and validate against clinical benchmarks.
- Scientific Communication: Present research at forums and prepare manuscripts for publication.
- Academic Growth: Engage in grant preparation and collaborative research.
How to Apply
Interested applicants should send their CV, cover letter, and relevant documents to Dr. Katharina Höbel at hertieai@medizin.uni-tuebingen.de. Please include the Index Number 7601 in your application.
Last Date for Application
17 June 2026
This scholarship represents a unique opportunity to be part of advanced research in medical imaging and artificial intelligence, contributing to significant advancements in brain health diagnostics.
