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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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. Simulation-based inference (SBI) addresses this by training neural networks, such as flow-matching generative models, on simulated events. The project aims to develop efficient, robust and calibrated SBI
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in large pre-trained models (vision-language models), generative models (flow matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of
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structure – function relationships in lipid systems for drug delivery (predominantly lipid nanoparticles). The group has extensive expertise using large scale research infrastructures to study lipid systems
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identity and extracellular signaling, but how cell types, signaling mechanisms, and transcription factors jointly determine tissue structure is incompletely understood. The Koplev lab is recruiting
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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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leading the investigation of structural and biophysical properties of miniaturized tumor environment models. A multidisciplinary approach is expected, integrating microfabrication, cell component and