The Laboratory of Computational Biology (www.aertslab.org) is part of the KU Leuven Center for Human Genetics and the VIB Center for Brain and Disease Research. We are interested in decoding the genomic regulatory code and understanding how genomic regulatory programs drive dynamic changes in cellular states, both in normal and disease processes. We use computational methods to decipher the gene regulatory logic underlying cell identity, focusing on cis-regulatory regions such as enhancers and promoters. We use a variety of computational techniques, including topic modelling, gene regulatory network inference, and deep learning. Our biological interest is broad, we study gene regulation in the vertebrate and invertebrate brain, in the neural tube, in cancer, and across the entire fruit fly (aka the Fly Cell Atlas).
Within our lab, we are seeking a computational biologist to work on a variety of large-scale projects in single-cell regulatory genomics, from the human brain to the Fly Cell Atlas. You will work across different teams and collaborate with Ph.D. students and postdocs in the lab, providing essential input on computational biology and machine-learning aspects of their research projects. In this job you will be confronted with exciting research questions in neurobiology, cancer, development, and evolution; and with a broad spectrum of computational challenges, single-cell and spatial multi-omics data sets, and artificial intelligence. You can interact with a broad bioinformatics and AI community in Leuven, allowing daily interactions with colleague bioinformaticians.
For recent publications see https://aertslab.org/#publications
- SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. BioRxiv 2022.
- Cell type directed design of synthetic enhancers. BioRxiv 2022.
- Decoding gene regulation in the fly brain. Nature 2022.
- Fly Cell Atlas. Science 2022.
- Cross-species analysis of enhancer logic using deep learning. Genome Research 2020.
- Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma. Nature Cell Biology 2020.
- cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nature Methods 2019.
- A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain. Cell 2018.
- SCENIC: single-cell regulatory network inference and clustering. Nature Methods 2017.
- MSc or Ph.D. degree in Bioinformatics, Engineering, Life sciences, or computational field
- Solid experience with programming (Python, R)
- Thorough understanding of molecular and developmental biology
Desirable but not required
- Knowledge of machine learning and ML libraries (Tensorflow/Pytorch) is a plus
- Experience with the analysis of high-throughput data, including transcriptomics, genomics, and single-cell RNA-seq data is a plus
Key personal characteristics
- You have a scientific attitude.
- You are meticulous, and proactive and can easily adapt to changing circumstances.
- You are endowed with a positive, communicative, and social personality.
- You have excellent interpersonal communication skills.
- You are eager to expand your skills in new technologies.
- You are a team player who is passionate about science and enthusiastic to be part of a dynamic research center.
- The ability to develop yourself further as an expert in single-cell bioinformatics and machine learning
- The ability to work on high-impact, state-of-the-art projects
- A stimulating and social international research environment with world-class science
- An open-ended contract with competitive salary and benefits
How to apply?
For more information contact: Stein Aerts, email@example.com
Please complete the online application procedure and include a detailed CV incl. a list of publications, a motivation letter, and the contact information of three referees.
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