Postdoc position in bioinformatics: gene regulatory network inference

Stockholm University
Sweden Stockholm


The goal of the project is to use gene regulatory network inference to discover new regulatory
mechanisms that control the repertoire of RNA binding proteins (RBPs). To this end, software
will be developed and applied in order to infer reliable gene regulatory networks (GRNs) and
to analyze the results. Both in-house and public data will be used to infer GRNs that will be
analyzed for regulatory links in relation to known RBP functions, and novel discoveries will be
experimentally validated.
This is a collaborative project between professors Erik Sonnhammer and Claudia Kutter, both
at SciLifeLab in Stockholm, Sweden. SciLifeLab is a national center for large-scale life science
research. The Sonnhammer group has extensive experience in GRN inference and has
developed several new algorithms to improve the reliability of the GRNs inferred from
perturbations. This includes the NestBoot algorithm applied to e.g. LASSO and Least Squares
inference methods. The Kutter lab has a research focus on RNA binding proteins and their
bound RNAs, especially lncRNAs. The project aims to infer a reliable GRN mainly from
shRNA-RNAseq perturbation data from ENCODE and in-house data from the Kutter lab. The
inferred GRNs will be analyzed for predictiveness and how well they replicate known links,
and newly discovered regulatory interactions will be subjected to experimental validation based
on their scientific value.


The project involves programming, data analysis, benchmarking, and modelling, as well as
application of the developed methods to experimental data. The successful candidate should be
highly motivated and have a Ph.D. in bioinformatics or related field, and good knowledge of
molecular biology. Alternatively, a Ph.D. in molecular biology or related field and 2 years of
postdoctoral experience in bioinformatics research and programming, documented by scientific
publications. Demonstrable familiarity with sequence and gene expression data analysis
techniques is essential. Excellent skills in computer programming (primarily Matlab, Python, R)
and UNIX are necessary merits.

Start date

As soon as possible

How to Apply

To apply, send your CV, a cover letter, and the email address of 2 references to The position is fully funded for 2 years of full-time study and
offers a competitive salary and excellent computational resources. For further information
about the research project, contact or See and


Erik Sonnhammer

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