We perform research on computational methods for analysis of epigenomics and transcriptomics data. Over the past years, we have developed methods for prediction of cell specific binding sites from open chromatin data (Gusmao et al., Bioinformatics, 2015; Nature Methods, 2016), inference of regulatory networks driving cell differentiation (Lin et al., NAR, 2015; Allhoff et al., Bioinformatics, 2015) and prediction of long noncoding RNA regulation via DNA-RNA interaction (Kalwa et al., NAR, 2016; Senturk et al., NAR, 2019).
We invite applicants for a Posdoc or Phd Candidate positions in:
1 – machine learning methods for the analysis of single cell sequencing
2 – algorithms for detection of RNA-DNA interactions
Candidates will perform research on methods for analysis of single cell epigenomics and transcriptomics (scRNA-seq and scATAC-seq) or novel protocols for detection of RNA-DNA interactions (TriplexRNA-seq, Capture-Seq, GRID-seq). The projects are performed in collaboration with medical specialists from the RWTH Aachen University for understanding cellular changes during inflammation and fibrosis and their support to cancer.
Applicants should hold a M.Sc. (Ph.D.) in Bioinformatics or Computer Science. Experience in the analysis of biological sequences, regulatory genomics and/or machine learning is required. The candidate should have solid programming skills (C, Python and/or R) and
acquaintance with Linux. Experience with high performance computing is a plus. The working language of the group is English.
Interested candidates should send a brief statement of research interests, CV and
the names of three references to email@example.com.