Host-pathogen interactions determining the outcome of infections are complex biological processes, governed by the interplay of diverse factors across multiple spatial and temporal scales. While data (e.g. from imaging, serology, multi-omics) are becoming available at an unprecedented level of detail, their integration and interpretation is challenging. Computational models, e.g. using differential equations or hybrid discrete-continuous descriptions, facilitate a mechanistic understanding, and are used to study e.g. the response of individuals to virus infections and vaccinations, or the interplay of pathogens and immune cells. However, statistical inference for such models is computationally demanding and does not scale to the steadily growing datasets from large clinical cohorts and high-throughput technologies.
In this project, set in the context of the BMBF project EMUNE, we build a framework for scalable statistical inference of host-pathogen interactions, combining novel concepts from machine learning with mechanistic modeling. Specifically, we develop methods based on invertible neural networks (INN) to describe probabilistic parameter-data relationships, for large but incomplete datasets. Based on these, we develop scalable marginalization and inference methods for non-linear mixed-effect (NLME) models. We apply these models to large-scale epidemiological datasets, e.g. from the pan-European SARS-CoV-2 study ORCHESTRA.
The deadline for the application round is March 15, 2022.
Application documents (cover letter, CV, certificates, two reference letters) should be submitted as soon as possible as a single PDF file via email.
Contact: Prof. Dr. Jan Hasenauer, firstname.lastname@example.org
Tagged as: Computer Science, Life Sciences, Mathematics
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