Helmholtz-Zentrum Münchenhelmholtz-munich.de
AI Scientist (f/m/x) – Virtual Tissue Staining & Cancer Diagnostics
- Standort
- Oberschleißheim
- Remote
- Hybrid
- Anstellungsart
- Vollzeit, Befristet/Zeitarbeit
- Erfahrung
- Mid-Level
- Bereich
- Software, Daten & IT
vor 4 Tagen
At Helmholtz Munich, we develop groundbreaking solutions for a healthier society in a rapidly changing world. We believe that diverse perspectives drive innovation. Through strong partnerships, we accelerate the transfer of new ideas from the lab to real-life applications, improving lives.
At the Institute for Intelligent Biotechnologies (iBIO) our major goal is to accelerate biomedical research by developing and combining enabling technologies. Our major research fields are AI-based organ mapping, tissue engineering and precision medicine in cancer treatments. Sophisticated technologies such as 3D tissue imaging or molecular profiling are combined with AI tools to decode the complexity of tissues and shed light into cancer biology.
Our interdisciplinary team — comprising biologists, engineers, computer scientists, and medical researchers — develops next-generation computational models to interpret complex biomedical data across multiple scales. Our innovations in tissue clearing, 3D microscopy, and AI-driven pathology have been featured in leading journals (Nature Methods, Nature Neuroscience, Cell, Nature Biotechnology) and major media (The New York Times, Wall Street Journal, Süddeutsche Zeitung).
Learn more about our research at www.erturk-lab.com.
We are seeking a highly motivated AI Scientist (f/m/x) (best: Computational Pathologist / Machine Learning Scientist for Digital Pathology) to join our efforts in developing AI-driven virtual staining pipelines for cancer tissue analysis.
Your tasks
- Develop AI-driven virtual staining pipelines for the analysis of cancer tissue samples
- Design, train, and optimize generative AI models that transform unlabeled or 3D tissue images into realistic virtual stains, such as synthetic H&E images or biomarker maps
- Develop and implement image processing, data preparation, and model evaluation workflows for complex biomedical imaging datasets
- Benchmark and validate model performance, robustness, and biological relevance in close collaboration with biologists, medical researchers, and imaging experts
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