Bioinformatician – Single-Cell Omics in Vascular & Skeletal Muscle Biology
100%, Zurich, fixed-term
The Laboratory of Exercise and Health, headed by Prof. Dr. Katrien De Bock at the Department of Health Sciences and Technology (D-HEST), ETH Zurich, is offering a fixed-term position for a bioinformatician (postdoctoral level) to lead the single-cell and spatial transcriptomics analyses within the laboratory of Exercise and Health.
Project background
The Laboratory of Exercise and Health investigates how the muscle microvascular niche – and in particular heterogeneous and specialized endothelial cell (EC) subpopulations – interacts with other niche cells (e.g. macrophages, pericytes, fibro-adipogenic progenitors) to maintain skeletal muscle homeostasis, and how disruption of this crosstalk contributes to muscle dysfunction. The research of the Laboratory of Exercise and Health combines human patient samples, mouse genetic models, single-cell and spatial transcriptomics, and in vivo cell-cell interaction tracing with CRISPR perturbation to mechanistically dissect EC-niche communication.
The lab offers a highly collaborative, international research environment at the interface of vascular biology, muscle physiology, and metabolism. For more information on the lab, visit www.musec.ethz.ch
Job description
You will lead all single-cell and spatial transcriptomics work, working closely with an international team of PhD students and postdocs and in direct collaboration with Prof. De Bock. Specific responsibilities include:
- Design, execute, and interpret scRNAseq analyses of human (patient-derived) and mouse skeletal muscle samples, identifying and annotating cell (sub)populations. Discuss and design follow-up wet lab experiments with colleagues.
- Integrate scRNAseq datasets with other omics information, including spatial transcriptomics, ATAC sequencing, metabolomics and/or multiplex imaging data.
- Perform cross-species (human–mouse) data integration to identify conserved and divergent cellular and molecular signatures of PAD.
- Apply and further develop computational tools for cell-cell communication analysis (ligand-receptor interactions and signaling pathways) and constraint based metabolic modeling tools (e.g. COMPASS, scFEA) to link transcriptional states to cell metabolism.
- Analyze single-cell datasets generated from in vivo cell-cell interaction tracing models.
- Perform pseudobulk and differential expression analyses (e.g. edgeR) of scRNAseq/5’ECCITE-seq data from CRISPR perturbation screens.
- Apply in silico perturbation approaches to nominate candidate regulators of cellular crosstalk for downstream functional validation.
- Build, document, and maintain reproducible bioinformatics pipelines, and support other lab members with transcriptomics and genomics data.
- Designing Shiny apps for data visualization for internal and possibly external use in publications.
- Contribute to manuscript preparation, data visualization, and project reporting, and stay current with emerging single-cell and spatial genomics methods.
Profile
- PhD degree in bioinformatics, computational biology, computational genomics, or a related quantitative field.
- Demonstrated hands-on experience analyzing single-cell RNA sequencing data (e.g. Seurat, Scanpy) from raw data processing through downstream analysis; experience with spatial transcriptomics is a strong plus.
- Excellent scripting and data analysis skills in R and/or Python, with in-depth knowledge of relevant Bioconductor/scverse packages.
- Strong experience with collaborative development environments (e.g. GitHub/GitLab), version control, and maintaining well-documented, reusable repositories.
- Working experience with high-performance computing environments.
- A strong interest in skeletal muscle physiology and/or vascular biology; prior research exposure to these fields is a plus but not required.
- Good understanding of biological research questions and enthusiasm for close collaboration with wet-lab researchers.
- Strong communication skills, a team-oriented and service-minded approach, and the ability to work independently and take ownership of a research area.
- Advanced proficiency in English.
Workplace
Workplace
We offer
A unique opportunity to lead the single-cell genomics workflow in a dynamic, collaborative, and internationally oriented lab. You will have access to state-of-the-art infrastructure at ETH Zurich, dedicated computational resources, and close interactions with other bio-informaticians within the institute as well as the Functional Genomics Center Zurich (www.fgcz.ch). We offer a competitive salary in line with ETH Zurich regulations.
In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity, and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Equal Opportunities and Diversity website
We value diversity and sustainability
In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.
Curious? So are we.
We look forward to receiving your online application with the following documents:
- CV
- A brief statement of motivation and research interests (max. one page)
- Contact details of two references
Further information about the lab can be found at our website or via LinkedIn (Katrien De Bock). Questions regarding the position should be directed to Prof. Dr. Katrien De Bock, [email protected] (no applications).
Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.