Data analyst in spatial single-cell biology

Gefunden in: beBee S CH - vor 4 Wochen

Zürich, Schweiz Springer Nature Vollzeit
Data analyst in spatial single-cell biology

The Bodenmiller laboratory at the University of Zurich and ETH Zürich is recruiting a computational biologist or bioinformatician to support the analysis of high-dimensional and spatially resolved single-cell data.

About the lab

The Bodenmiller group is among the world-leading laboratories in development and application of high-dimensional cytometry approaches to enable novel analyses of human tissues. With a specific focus on oncology, we strive to better understand cancer development to enable personalized treatments. Our group pioneered highly multiplexed imaging and suspension mass cytometry approaches to comprehensively profile properties of tumor ecosystems including interactions between the various cell types present in tumors and tumor microenvironments. We are a highly collaborative and interdisciplinary team of experimentalists and computational biologists with a clear vision to change how tumors are analyzed to ultimately improve patients’ lives. The successful applicant will share the collaborative spirit, actively support the research of the lab, and be eager to engage in transformative research.

Your role

  • Analyze large spatial single-cell datasets Develop new computational methods for image and spatial data analysis Maintain data analysis workflows and our code base for multiplexed single-cell expression data (imaging mass cytometry, CyTOF, single-cell RNA sequencing, spatial transcriptomics) Benchmark computational tools to enhance single-cell data analysis Support efforts by lab members to develop new analysis tools

Requirements

  • Master degree, PhD or equivalent qualification in computational biology, bioinformatics, computer science, statistics, or similar Solid knowledge and experience with analysis of high-dimensional and/or large-scale biological and/or medical datasets Experience with single-cell data analysis and image analysis Solid skills in R/Python programming, version control and environment management (git, conda) Code containerization (docker) Strong interest in life science and medical research A basic understanding of molecular biology Strong communication skills that will enable effective participation in an interdisciplinary team of experimentalists and computational biologists

Optional skills considered as a plus:

  • Experience in software/package development in R (Bioconductor) and/or Python Experience with spatial statistics Experience in high-performance computing and automated workflow development (e.g. snakemake, workflow) Experience with convolutional neural networks, autoencoders or transformer models


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