Postdoc or PhD Candidate (f/m/x) - AI Augmented Target Discovery in Translational Kidney Disease

• Build and analyze a translational kidney disease atlas by integrating single-cell data across species and diseases.
• Implement a framework to assess organoid disease model translatability to human patient data.
• Design large scale perturbation screening strategies, combining cost-effective technologies with single-cell RNA-seq and imaging data.
• Generate a kidney disease perturbation atlas and train AI models to develop world-leading approaches for in silico perturbation prediction, while benchmarking existing models.
• Contribute to the integration of computational predictions with wet-lab validation to ensure translational impact of the findings.

• Currently pursuing or recently obtained a PhD, Master’s degree, or equivalent in Computer Science, Bioinformatics, Computational Biology, Biology, or a related field.
• Strong programming skills in data science workflows (e.g. Python, GitHub) and motivation to learn and apply modern deep learning frameworks (e.g. PyTorch).
• Solid understanding of molecular biology and a strong interest in complex disease models; experience with experimental techniques is welcome but not required.
• High scientific curiosity and motivation to contribute to AI-augmented disease atlases and perturbation models, combined with excellent communication skills and the ability to collaborate in interdisciplinary teams.

Desirable qualifications:

• Experience in identifying, curating, and integrating large-scale, multimodal biological or clinical datasets.
• Familiarity with single-cell data analysis, foundation models, multi-modal integration, perturbation experiments, organoid models, or translational target discovery.
• Exposure to active learning, iterative screening, perturbation data, or target identification steps in drug development.