Postdoc: AI for high-dimensional cardiovascular data
The Data Science and its Applications research group is 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.
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.
In collaboration with partners, we are interested in cardiac events, such as atherosclerosis and atherothrombosis. To this end, multidimensional datasets at protein, lipid and metabolite levels together with bioinformatics workflows, machine learning and multi-OMICS data integration enable comprehensive characterization of clinical samples and deciphering of complex pathophysiological mechanisms in different disease settings.
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.