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, calibration and reliability of large pre-trained models Probabilistic generative models and world models Probabilistic machine learning for scientific discovery Don’t see your exact idea listed? We encourage
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matter (or other “invisible” new physics signals) at the Large Hadron Collider (LHC) compare high-dimensional collision data against detailed simulations whose likelihood cannot be evaluated, only sampled
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platforms and evaluation of new materials in interaction with biological systems to understand the underlying principles. For us, it is equally important to study the impact of materials on biological
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external large-scale datasets. The role focuses on establishing the data foundations for the program’s initial modelling efforts by identifying, evaluating, integrating, and structuring large biological
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for single-nucleus RNA sequencing and chromatin accessibility assays Systematically evaluate and improve data quality, signal-to-noise ratios and reproducibility in experimental workflows Identify critical