About the team/job
About the team: The research group of Wolfgang Huber at EMBL works on biological data science and mathematical/computational method development. The interdisciplinary and international team has strong collaborations with researchers in cancer and developmental biology and uses cutting edge experimental data for statistical computing and probabilistic modelling to enable discovery and understanding of biological principles and biomedical applications.
About the project: Project DECODE aims to comprehensively map, model and understand genetic networks at single cell resolution in complex tissues of organisms in vivo. The project combines high-throughput reverse genetics by inducible CRISPR-Cas9 gene knockout, single-cell RNA-seq (Perturb-Seq), imaging-based phenotyping, statistical data analysis, and mathematical and computational modelling. To drive this new, ERC Synergy funded collaboration between the experimental labs of Michael Boutros (DKFZ) and Jan Lohmann (University of Heidelberg) and the computational groups of Wolfgang Huber (EMBL) and Oliver Stegle (DKFZ & EMBL), we are looking for postdocs in the areas of biological data science, probabilistic modelling, statistical inference and scientific computing.
As part of the research team, you will participate in the overall project and shape your own research profile by engaging in one or several of the following areas:
- Conducting integrative data analysis for biological discovery in
- Single-cell RNA-seq data analysis at ultra-large scale
- High-throughput CRISPR-KO perturbation analysis
- Imaging-based phenotyping
- Developing novel computational methods using multivariate statistics, Bayesian inference or dimension reduction
- Modelling/investigating cell states, transitions and genetic dependencies
- Modelling gene-gene and gene-environment interactions
- Dimension reduction and interactive visualization
- Data science, quality assurance and model checking
- Developing scalable and robust software for scientific computing
- Probabilistic modelling of genetic causal networks using latent spaces, categories, graphs and dynamic processes
Further background information:
A PhD or equivalent qualification in a quantitative science (mathematics, statistics, physics, computer science, computational biology). We are looking for a range of talents, which should include some of the following: strong theoretical foundations in probability, statistics, linear algebra and geometry; high-dimensional statistics, machine learning and Bayesian approaches; biological data science and data-driven discovery; scientific programming. Applications from “newcomers” into biology are welcome.
You might also have
You are excited by making or contributing to biological discoveries, you are interested in interdisciplinary science, enjoy collaborative work and like to communicate concepts and results to other scientists in different fields of research.
Why join us
EMBL is an inclusive, equal opportunity employer offering attractive conditions and benefits appropriate to an international research organisation with a very collegial and family friendly working environment. The remuneration package comprises from a competitive salary, a comprehensive pension scheme, medical, educational and other social benefits, and the availability of an excellent child care facility on campus.
What else do I need to know
We are Europe’s flagship research laboratory for the life sciences – an intergovernmental organisation performing scientific research in disciplines including molecular biology, physics, chemistry and computer science. We are an international, innovative and interdisciplinary laboratory with more than 1600 employees from many nations, operating across six sites, in Heidelberg (HQ), Barcelona, Hinxton near Cambridge, Hamburg, Grenoble and Rome.
Our mission is to offer vital services in training scientists, students and visitors at all levels; to develop new instruments and methods in the life sciences and actively engage in technology transfer activities, and to integrate European life science research.