Postdoctoral Researcher in Computational Genomics and Machine Learning
Postdoctoral Researcher in Computational Genomics and Machine Learning
University of Helsinki
Helsinki, Finland
Summary of the project
Human tumors are characterized by recurrent mutations in the well-defined cancer driver genes, but also non-mutational epigenetic reprogramming and phenotypic plasticity controlled by transcription factors (TF) are important contributors to tumorigenesis. Extensive consortium efforts have mapped the mutational and epigenetic landscapes of human cancers. However, the commonly used epigenomics methods such as ChIP-seq are correlative in nature and report a lot of spurious sites that might not be linked to gene expression, highlighting the need for novel innovative approaches to understand cancer genome and epigenome. Here, we will utilize cutting-edge long-read nanopore sequencing combined with deep machine learning approaches to model the epigenetic changes and mutational mechanisms in cancers originating from endodermal tissues. The goal is to reveal novel mechanistic details about the development of human cancer.
Tasks
The postdoctoral researcher will analyze NaNOME-seq, ATAC-seq and RNA-seq data to model the gene regulatory logic in epithelial cells. In addition, the researcher will investigate the effects of DNA adducts on methylome, transcriptome, and the gene regulatory logic.
Skills, expertise and/or qualifications
We expect a successful candidate to have a PhD degree from a relevant field with skills and experience in computational genomics and machine learning. Familiarity with the above-mentioned data types is an asset, as well as a strong publication record in a relevant field. Good written and oral communication skills in English are expected.
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