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. Preferred qualifications: Strong methodological training in statistical inference, computational statistics, reinforcement learning, or optimization. Experience with Bayesian statistics, adaptive clinical
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that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics
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heterogeneity in cancer, inflammation, and tissue senescence. • Developing next-generation deep-learning and statistical deconvolution methods for inferring gene regulation from bulk, single-cell, and spatial
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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, or multitrophic interactions is a strong merit. Experience with computational methods such as multilayer networks, Bayesian inference, or higher-order network models is also a merit. The ability to communicate