About this position
Area of research:
Scientific / postdoctoral posts
Job description: AI Scientist (f/m/x) – Virtual Tissue Staining & Cancer Diagnostics
102994
Part time
29,5 hrs./week
Neuherberg near Munich
Partially Remote Work possible
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
- Contribute to the integration of AI-based image analysis with microscopy, histopathology, and molecular data to generate new insights into cancer biology
- Present and publish research findings and contribute to the further development of AI-driven technologies for biomedical research and cancer diagnostics
Your profile
- Master's degree in a relevant field
- Experience in machine learning for imaging, ideally in biomedical or histopathological contexts
- Strong programming skills and deep learning frameworks
- Familiarity with generative models (e.g., diffusion, GANs, autoencoders) or virtual staining concepts
- Excellent teamwork and communication skills in an international environment
Desirable qualifications
- Understanding of microscopy, image analysis, and biological data (H&E, immunostaining, omics integration)
- Experience with 3D networks, transformer architectures, or self-supervised learning
If you fulfill all the requirements, you may be eligible for a salary grade of up to E 13. Social benefits are based on the Collective Wage Agreement for Public-Sector Employees (TVöD). This position is for a fixed term of 1 year.
Managing Director Mike Frieser: „We believe that diversity is the key to groundbreaking research and innovative solutions. Our flexible working models and remote options create a supportive environment for balancing work and family life. For us, contributing to a healthier society in such an inspiring workplace is truly meaningful.“
Get to know us
Benefits
- Career Development: Postdoc program, scientific training & career center with tailored offers
- Scientific Resources: State-of-the-art infrastruture and Core Facilities
- Recreation: 30 days annual leave, flexi days, plus public holidays
- International Staff Service: Support with the relocation and integration process in Germany
- Health Promotion: Sports, company doctor, mental health initiatives
Interested in applying?
If you have any questions, feel free to contact Stefanie Reitinger, stefanie.reitinger@helmholtz-munich.de, who will be happy to help.
Our recruiting is decentralized - your application will be reviewed directly by the specialist department in which you could work in the future.
More information about our application process
Please send your application only via our online application tool and with the following documents:
- CV
- Cover letter
- Degrees/Diplomas/Certificates
- Contact details for at least two referees
If you have obtained a university degree abroad, we will require further documents from you regarding the comparability of your degree at the latest by the time you start work.
Please request the Statement of Comparability for Foreign Higher Education Qualifications as early as possible.
We are committed to promoting a culture of diversity and welcome applications from talented people regardless of gender, cultural background, nationality, ethnicity, sexual identity, physical abilities, religion or age. In case of equal qualifications, preference will be given to applicants with severe disabilities.
Learn more about diversity at Helmholtz Munich
Our commitment
- Helmholtz Munich
- Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)
- Institute for Intelligent Biotechnologies
- Ingolstädter Landstraße 1
- 85764 Neuherberg
This research center is part of the Helmholtz Association of German Research Centers. With more than 42,000 employees and an annual budget of over € 5 billion, the Helmholtz Association is Germany's largest scientific organisation.
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Email the contact below with your CV and a short, specific message. See Emailing Professors.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.