Postdoc: AI for high-dimensional Covid-19 data

University Medical Center Mainz and TU Kaiserslautern are looking for a postdoc for a project on AI methods to high-dimensional biomedical data to promote understanding of biological processes associated with cardiovascular illness and Sars-CoV-2. The candidate will be affiliated with both institutions and could be physically based in either one, as preferred by the postdoc.

The research will be supported by an interdisciplinary team with expertise in Systems Medicine (Prof. Philipp Wild, University Medical Center Mainz) and Artificial Intelligence (Prof. Dr. Sebastian Vollmer, DFKI/TU Kaiserslautern).

The project involves highly granular data collected as part of the Gutenberg Health Study—a prospective cohort study of a representative sample (N=15,000) of the population of Mainz — including genotyping, DNA methylation, transcriptomics, proteomics and extensive time-varying clinical information.

A key aim of the research is to investigate mechanisms and effects of Covid-19 among some 500 participants who tested positive for the virus. How does the molecular profile (especially the proteomic profile) change following Sars-CoV-2 infection? What mechanisms distinguish symptomatic and asymptomatic infections? Which trajectories and processes are associated with severe disease outcomes or long-term effects?

In collaboration with partners, we will also investigate the role of auto-immunity in Sars-CoV-2 infection.

An ideal candidate has a background or an interest in molecular epidemiology, bioinformatics or biostatistics, allowing direct interpretation of the results. They should have experience or interest in high-dimensional data analysis, especially applying machine learning methods to multi-omics data.

For further information, please visit the full job advertisement and please do not hesitate to contact us if you have any questions.

There are also other opportunities available; see the University Medical Center Mainz web site for more.

Sebastian Vollmer
Sebastian Vollmer
Professor for Applications of Machine Learning

My research interests lie at the interface of applied probability, statistical inference and machine learning.