Postdoctoral AI Scientist / Team LeadInstitute for Intelligent Biotechnologies, Helmholtz Munich / LMU Munich
The Institute for Intelligent Biotechnologies (iBIO) at Helmholtz Munich and LMU Munich is looking for an outstanding Postdoctoral AI Scientist with a strong background in computer vision, deep learning, and large-scale biological image analysis and omics .
We are building AI-driven, whole-body atlases of mammalian biology by combining the world’s largest and highest-resolution 3D imaging datasets with omics, spatial molecular profiling, perturbation models, and disease biology. The successful candidate will help lead the computational development of this platform and guide a growing team working at the interface of AI, imaging, omics, and systems biology.
Position
This is a senior postdoctoral position for a candidate who can take scientific and technical ownership of major AI projects. The ideal candidate has a PhD in computer science, AI, machine learning, computational biology, bioinformatics, or a related quantitative field, with several years of hands-on experience in computer vision and deep learning.
The candidate should be able to lead projects independently, supervise students, coordinate with experimental and imaging teams, and help guide a team of approximately six PhD students working on AI-driven whole-body biology.
Experience with omics data, including single-cell RNA-seq, spatial transcriptomics, proteomics, or multimodal data integration, is a strong plus but not mandatory.
AI and Computer Science at iBIO
Our lab develops new algorithms, benchmarks, datasets, and software for large-scale multimodal biology. We publish in leading venues in AI, computer science, biology, and medicine.
Examples of our recent AI and computational work include:
* MouseMapper, deep learning ensemble for whole body mapping, Nature, 2026
* Graph neural networks for spatial molecular profiles, Nature Communications, 2025
* Whole-body deep learning delivery benchmarking at single-cell resolution, Nature Biotechnology, 2025
* SELMA3D challenge on self-supervised learning for 3D light-sheet microscopy segmentation, arXiv, 2025
* VR-empowered deep learning analysis of brain cells, Nature Methods, 2024
* Whole-brain vessel graph datasets and benchmarks for graph learning, NeurIPS Datasets & Benchmarks / arXiv, 2021
* Machine learning analysis of whole mouse brain vasculature, Nature Methods, 2020
* Deep learning enabled multi-organ segmentation in whole-body mouse scans, Nature Communications, 2020
* Deep learning for whole-body cancer metastasis analysis, Cell, 2019
Scientific Focus
You will contribute to the development of AI models that integrate and analyze:
* Whole-body, single-cell-resolution 3D imaging data
* Large-scale light-sheet microscopy datasets
* Cell segmentation, registration, tracking, and phenotyping pipelines
* Multimodal imaging and omics datasets
* Single-cell and single-nucleus RNA-seq
* Spatial transcriptomics
* Proteomics and spatial proteomics
* Genetic perturbation and disease models
The goal is to create predictive, AI-driven atlases of mammalian biology that map cells, tissues, organs, and molecular programs across the entire body, and to use these models to understand disease mechanisms and therapy responses.
Your Tasks
* Lead the development of deep learning and computer vision and spatial omics pipelines for large-scale 3D biological mapping
* Build AI models for segmentation, registration, representation learning, multimodal integration, and disease prediction
* Align whole-body imaging maps with omics and spatial molecular data
* Develop scalable tools for atlas generation, perturbation analysis, and cell-state mapping
* Supervise and mentor PhD students, master students, and junior AI researchers
* Help coordinate a team of approximately six PhD students working on AI, imaging, and omics projects
* Translate biological questions into computational strategies and robust analysis pipelines
* Work closely with experimental, imaging, omics, and disease biology teams
* Contribute to high-impact publications, software releases, datasets, and benchmarks
Your Profile
Required:
* PhD in computer science, AI, machine learning, computational biology, bioinformatics, biomedical image analysis, or a related field
* Several years of hands-on experience in computer vision and deep learning
* Strong programming skills, especially in Python and modern ML frameworks such as PyTorch or TensorFlow
* Experience with large-scale image analysis, ideally 3D microscopy, medical imaging, biological imaging, or spatial data
* Proven ability to lead scientific projects independently
* Experience supervising master students, PhD students, or junior researchers
* Strong publication record or clear evidence of excellent technical output
* Ability to work across disciplines and communicate with both computational and experimental scientists
* Independent, structured, reliable, and motivated to build ambitious scientific platforms
Strong advantages:
* Experience with omics data analysis, especially scRNA-seq, spatial transcriptomics, proteomics, or multimodal data integration
* Familiarity with Scanpy, Seurat, Bioconductor, spatial omics toolchains, or related frameworks
* Experience with graph neural networks, foundation models, self-supervised learning, representation learning, or multimodal AI
* Experience managing codebases, datasets, benchmarks, or collaborative software projects
* Interest in whole-body biology, disease mechanisms, digital twins, and predictive models of mammalian systems
Supervision and Hiring Team
Hiring Manager: Prof. Dr. Ali Ertürk
AI Team Lead: Dr. Ying Chen
Hiring Administration: Stefanie Reitinger
What We Offer
* Access to unique whole-body imaging and multi-omics datasets
* Large-scale computing infrastructure, including dedicated A100 clusters and petabyte-scale storage
* A leading role in building next-generation AI models for mammalian biology
* Close interaction with experimental, imaging, omics, and disease biology teams
* Strong interdisciplinary supervision and scientific visibility
* Opportunity to lead projects, mentor junior scientists, and shape the AI direction of iBIO
* International research environment at Helmholtz Munich and LMU Munich
* Competitive salary according to institutional regulations
Environment
iBIO at Helmholtz Munich and LMU Munich combines advanced tissue clearing, light-sheet microscopy, omics, and AI to build digital, predictive models of mammalian biology, from cell to whole organism.
The lab develops technologies to map entire mammalian bodies at cellular resolution and uses AI to understand disease, perturbation, therapy response, and biological organization across organs and systems.
Application
Please submit your application as one single PDF only. This PDF should include your CV and a cover letter explaining why you are an excellent fit for this position, how your experience matches our needs, and what you would bring to the role. This is the only document evaluated at 1st selection, even not your email content. Please do not include any additional attachments such as references, certificates, or other supporting documents. Applications that do not follow these instructions will be filtered out and ignored.
To:
Stefanie Reitinger
Hiring Administration, iBIO
Helmholtz Munich
Email: stefanie.reitinger@helmholtz-munich.de
Applications will be reviewed on a rolling basis until the position is filled.
IMPORTANT NOTE:
Subject:
“iBIO AI Postdoc/Team Lead (iBIO-AI-2026-06)”