Develop next‑generation AI systems that integrate population‑scale genetics and multimodal biomedical data. Build reusable foundation models, agentic workflows, and scalable research software to accelerate discovery. Require PhD, 4+ years research experience, strong Python and machine‑learning expertise.
Key Highlights
Design and implement AI foundations and agentic scientific systems for human health research
Create reusable, benchmarked AI capabilities and scalable software for large biomedical datasets
Collaborate internationally with AI, computational biology, and clinical groups
Key Responsibilities
Develop next‑generation AI systems at the intersection of biological research and frontier AI.
Transform emerging methods and models into robust, reusable AI capabilities such as foundation models and multimodal representation learning.
Build scalable research software, AI workflows, and evaluation frameworks for large heterogeneous scientific datasets.
Lead and contribute to high‑impact scientific publications, open‑source software, and reusable research tools.
Collaborate with European AI community partners, including Helmholtz Munich and the ELLIS network.
Engage with computational, biological, and clinical groups across the institute to drive interdisciplinary projects.
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Nice to Have
Industry or applied R&D experience as AI/ML architect or research scientist
Experience developing foundation models or large‑scale AI systems for biomedical applications
Experience with LLM‑based agents and agentic workflows for scientific tasks
Integration of multimodal biomedical datasets (molecular, imaging, clinical, population‑scale)
Leadership of collaborative software projects and contributions to open‑source scientific software
Workflow management, containerisation, and scalable GPU/HPC computing
Familiarity with human genetics and computational biology
Experience applying AI in drug discovery or translational research
