This position focuses on machine learning for regulatory genomics. As part of the TRR 267 Consortium 'Non-coding RNA in the cardiovascular system,' your primary responsibilities will revolve around advancing deep learning models of protein-RNA interactions, building upon existing methods developed within our lab. You will also have the opportunity to develop new AI tools to predict lnRNA subcellular localization, splicing, and the impact of RNA modifications from high-throughput genomic data generated within the Consortium, such as CLIP-seq data for protein-RNA interactions, bulk and single-cell RNA-seq data, and Massive Parallel Reporter Assays.
To be successful in this role, you should have a strong background in statistical modelling and machine learning, as well as bioinformatics. Proficiency in programming languages commonly used in bioinformatics and suitable for deep learning applications, such as Python, is essential. Previous experience with analysing genomic data will be advantageous. The candidate must have excellent written and verbal communication skills, the ability to engage in independent thinking, be a good team member and willing to actively collaborate with biologists. The candidate must also demonstrate scholarship through at least one published first-author manuscript.
To apply, please email (1) a cover letter describing current and future research interests, (2) a curriculum vitae, and (3) a list of at least two references to Dr. Annalisa Marsico at annalisa.marsico@helmholtz-muenchen.de. Non-specific applications without an expression of interest in the research of our lab will not be considered.