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.
- Development of statistical inference and machine learning methods, with focus on non-linear mixed-effect models and invertible neural networks
- Implementation of algorithms in open-source, reusable software packages
- Application of methods to biological data, with focus on infectious diseases such as SARS-CoV-2 and HIV
- Publication of results in scientific journals and at conferences
- Collaboration with national and international partners
- (As PostDoc) Co-supervision of students and assistance with teaching
- Master / PhD degree in (bio-)informatics, computational biology, computer science, mathematics, physics, or a related field
- Experience in some of the following fields: mathematical modeling, mixed-effect modeling, differential equations, statistical inference, numerical optimization, machine learning
- Programming skills in e.g. Python, Julia, C++, or R, and collaborative software development experience
- Proficiency in written and spoken English
- Passion for science and scientific work
- Working in an innovative, well-equipped and scientifically stimulating environment
- An international and diverse group of PhD students and PostDocs
- A professional career development program for both PhD students and PostDocs
- As PhD student, initial 3 year contract with a standard public service salary (75% TV EntgO Bund EG 13); as PostDoc initial 2 year contract (100% TV EntgO Bund EG 13)
- PostDocs will have opportunities to obtain additional external funding and develop an independent research program during postdoctoral training
Further training opportunities
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, [email protected]