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The Laboratory of Causal Systems Immunology is seeking a skilled bioinformatician to analyze single-cell and spatial omics datasets, develop flexible computational pipelines, and collaborate closely with experimental teams. You will work with RNA-seq, ATAC-seq and perturbational data, integrating results across project types.
The role offers a stimulating international environment, 80% or full-time appointment options, and access to state-of-the-art infrastructure, enabling advancement as you
The Laboratory of Causal Systems Immunology combines large-scale in vivo perturbation experiments, advanced mouse models of immune diseases and causal learning to uncover how genes shape immune-cell states and predict how the immune system responds to interventions.
This tight integration of experimental immunology with data-driven experiments allows us to tackle fundamental biological questions with direct biomedical relevance.
We are embedded in both the VIB-UGent Center for Inflammation Research, a leading center unravelling the cellular and molecular mechanisms of inflammatory diseases, and the VIB Center for AI & Computational Biology, a young, ambitious center whose research groups address biological questions through the lens of AI.
We have developed, and continue to advance, a set of untargeted and targeted spatial, single-cell, single-nucleus, and perturbational omics technologies. Our in vivo single-cell CRISPR technologies allow us to screen for dozens of molecular factors in vivo during development and disease. These technologies transform the speed, scale and resolution at which in vivo immune-cell pathways can be unraveled. By combining these approaches with single-nucleus ATAC-seq and spatial data, we are moving towards a comprehensive understanding of the regulatory mechanisms that shape liver cell states in health and disease, including metabolism, cancer, regeneration and development.
In this role, you will apply innovative computational approaches to analyze a broad range of single-cell and single-nucleus datasets, including RNA-seq, ATAC-seq and perturbational omics. You will also work with spatial transcriptomics data, using technologies such as MERFISH/MERSCOPE, Visium HD and ATERA. Depending on your interests and strengths, you can choose to focus more strongly on either single-cell and perturbational data analysis or spatial data analysis, while also gaining exposure to the broader range of technologies. If you have a particular interest or stronger experience in one of these areas, we would be very interested to hear about it.
Your work will involve developing, adapting and fine-tuning computational pipelines, often building on approaches developed within our lab or institute. You will work closely with both computational and experimental colleagues to analyze data in a detailed and scientifically sound manner, integrate and compare results across different data types and projects, and uncover biological patterns and insights. Strong collaboration and communication across the computational and experimental sides of the lab will be an important part of the role.
The ability to work on scientifically exceptional and high-impact research topics.