Two Postdocs in Single-Cell Bioinformatics and Machine Learning

Leuven VIB-KU Leuven Center for Brain & Disease Research

19 Sep 2017


VIB-KU Leuven Center for Brain & Disease Research

Stein Aerts Lab




The Laboratory of Computational Biology (Stein Aerts lab) is part of the VIB Center for Brain & Disease Research and the University of Leuven, Belgium. Our lab is a “humid” lab, half wet and half dry. In the wet-lab we apply high-throughput technologies to decipher enhancer logic and map gene regulatory networks, such as RNA-seq for transcriptomics and ATAC-seq and ChIP-seq for epigenomic profiling. To test the activities of promoters and enhancers we use massively parallel enhancer-reporter assays. Finally, to map high-resolution landscapes of possible cellular states we use single-cell transcriptomics and single-cell epigenomics.

We are interested in decoding transcriptional states, and understanding how cell fate is determined by complex gene regulatory networks. The nodes in these networks are cis-regulatory regions such as enhancers and promoters, where transcription factors bind to regulate the expression of their target genes.

Your goal will be to develop new bioinformatics and machine learning approaches to map gene regulatory networks and to decipher the genomic regulatory code underlying cell fate decisions. To this end, you will combine single-cell transcriptomics, epigenomics, and imaging data. As model systems you can use human or Drosophila, with a focus on the adult brain (ageing, neuro-degeneration) and cancer (tumor heterogeneity). Depending on your background and interests, you can also perform wet-lab experiments, including the use of microfluidics, imaging, and next-generation sequencing.

Relevant publications

  • Aibar et al. SCENIC: Single-cell regulatory network inference and clustering. Biorxiv 2017. Nature Methods, in press.
  • Verfaillie et al. Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state. Nature Communications 2015.
  • Verfaillie et al. Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic. Genome Research 2016

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We are seeking two highly skilled Postdocs in single-cell bioinformatics and machine learning.


  • Ph.D. in Bioinformatics, Computer Science, Physics, Bio-Engineering or similar
  • A strong scientific track record
  • Programming skills are a must


  • Experience with machine learning is a plus
  • Being able to combine bioinformatics with wet-lab is a plus, but not required

We offer:

  • A creative environment with a great diversity of researchers and technologies
  • A competitive compensation package based on expertise and experience
  • You start with a 2-year contract, but encouraged to apply for postdoctoral fellowships
  • Starting Date: as soon as possible

How to apply?

Please complete the online application procedure and include a detailed CV incl. list of publications, a motivation letter and the contact information of three referees.