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knowledge-based reasoning. The goal is to move beyond isolated prediction models towards an integrated agentic system that can model patient trajectories, compare treatment strategies and generate
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with deeply annotated outcome data across tens of thousands of cases (Ritter et al., Nat Cancer 2026; Maas et al., Nat Genetics 2026). Our recent work includes Hetairos, a histology-based AI model
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covering study design, data processing, statistical analyses, model evaluation, and interpretation of findings Investigate and optimize user experience and objective performance of AI systems in
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genetics of model gut bacteria and Drosophila with multi-omics approaches to understand how symbiosis supports essential aspects of animal physiology. We are also interested in the basic mechanisms
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) we model distinct NR2F1 dosages to study their effects across neuronal differentiation. By integrating time-resolved bulk (RNA-seq, ATAC-seq, ChIP-seq, Hi-C) obtained from neuronal lineages cultured
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learning frameworks (e. g., PyTorch, TensorFlow) and model training, evaluation and optimization on large-scale datasets Experience with bioimage analysis tools and environments (e.g., FIJI/ImageJ