Postdoctoral Scientist in Genome Control : Leuven, Belgium

The Laboratory of Computational Biology, headed by Prof. Stein Aerts, at the Center for Human Genetics, University of Leuven, is hiring a
Postdoctoral Scientist in Genome Control

We combine wet-lab and dry-lab experiments to study the cis-regulatory logic encoded in a Metazoan genome. We study developmental gene regulatory networks using Drosophila as model system; as well as cancer gene regulatory networks using human cancer samples (melanoma, leukemia) and Drosophila cancer models.

You will combine next-generation sequencing, integrative genomics, and computational models to unravel cis-regulatory codes and networks. Although your main focus is develop computational approaches, you will work in close collaboration with the wet-lab, enabling high-throughput experiments using RNA-Seq, ChIP-Seq, damID-Seq, DHS-Seq, FAIRE- Seq, STARR-Seq, and 4C-Seq/HiC. Your challenge is to integrate these data with computational predictions of cis-regulatory modules, and with publicly available data from (mod)ENCODE and TCGA, to gain new insight into genome control driving normal and oncogenic networks. If successful, you can apply your predictive models to re-sequenced cancer genomes to identify and prioritize cis-regulatory variation that impacts gene regulation and drives oncogenesis.

Qualifications:
• PhD degree in computational, mathematical, or engineering field
• Interested in the non-coding genome, and highly motivated
• Strong track record of publications in the field of computational biology, analysis of
high-throughput data sets, data integration
• Solid experience with bioinformatics programming and statistics for bioinformatics,
including scripting (e.g., Python) and R (incl. Bioconductor).
• Are considered as a plus: experience with pathway analysis, cis-regulatory sequence
analysis, understanding of molecular and developmental biology, expertise on cancer biology, experience with programming (e.g., C++, Java), experience with data mining and machine learning, classification, Hidden Markov Models, experience with next- generation sequence data (exome sequencing, genome sequencing, SNP/indel calling, CNV, structural variation; RNA-Seq, ChIP-Seq), statistical genetics, and eQTL analysis.

Candidates can apply by sending a motivation letter, CV, and contact details of two references tostein.aerts@med.kuleuven.be…