Please find below details for a postdoc opportunity in statistical methods development for single-cell genomics at EMBL Heidelberg.
Please contact me for further details.
Oliver Stegle, PhD - Research Group Leader
European Bioinformatics Institute (EMBL-EBI)
Wellcome Genome Campus, Cambridge CB10 1SD, UK
Web: www.ebi.ac.uk/research/stegle <http://www.ebi.ac.uk/research/stegle> | Phone: +44 1223 494 101
Genome Biology Unit
European Molecular Biology Laboratory (EMBL)
Web: www.embl.de/research/units/genome_biology/stegle <http://www.embl.de/research/units/genome_biology/stegle> | Phone: +49 6221 3878190
A Postdoctoral position in computational single-cell genomics is available in the Statistical Genomics and Systems Genetics group at our newly established location as part of the Genome Biology Unit at EMBL Heidelberg in Germany.
Our research group bridges the excellence in genomics and genetics at the Genome Campus in Hinxton, Cambridge, UK with molecular profiling techniques and statistical computing at EMBL Heidelberg, Germany. The postdoctoral fellow will develop and apply novel computational methods for interrogating single-cell RNA-seq and other single-cell variation datasets. The aims of this post are closely connected to the Human Cell Atlas, to which our group contributes as a node in the analysis working group. As a core aim of this project, we seek to develop computational strategies for the joint analysis of datasets with millions of cells, and to integrate spatial technologies with single-cell RNA-seq and epigenome methods.
The fellow will be located in the Stegle group and collaborate with partners in the Human Cell Atlas, collaborators at EMBL and elsewhere. We seek to build on previous developments and expertise in the group, including factor model, linear mixed models and deep learning methods (see below). The position will be primarily based at EMBL Heidelberg, however regular exchange and visits to the Genome Campus in Hinxton are facilitated by the dual location of the team.
Recent relevant publications:
Buettner, F., et al. (2017) f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq." Genome biology 18.1 (2017): 212.
Svensson, V., et al. (2018) SpatialDE: Identification of spatially variable genes. Nature Methods, advance online.
Angermueller, Christof, et al. (2017) DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome biology 18.1 (2017): 67.
Buettner, F., et al. (2015). Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nature biotechnology, 33(2), 155.
Argelaguet, R., et al. (2017). Multi-Omics factor analysis disentangles heterogeneity in blood cancer. bioRxiv, 217554.
EMBL is Europe’s flagship research organisation for the life sciences – an intergovernmental organisation with more than 80 independent research groups covering the spectrum of molecular biology. EMBL is international, innovative and interdisciplinary – its 1600 employees, from many nations, operate across six sites near Heidelberg, Hamburg, Grenoble, Rome, Cambridge and Barcelona.
Qualifications and Experience
The successful applicant will hold a doctoral degree or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical development.
Previous experience in developing and applying computational methods applied to large datasets is expected. Expertise in analysis and integration of multiomics data, statistical genetics, statistical interpretation and analysis of next-generation sequencing datasets is beneficial, as is communicating results in scientific conferences and papers.
We especially seek candidates with prior experience in developing statistical methodology in a genomics context, including gene expression analysis, factor models, GWAS and analysis of NGS data. A good foundation in, and previous usage of methods in any of the following fields is advantageous: statistics, machine learning, genetics, optimization and mathematical modelling. A background in molecular biology, or previous experience tackling biological questions is beneficial but not necessary.
Proficiency with a high-level programming language (e.g., C++, Java) and/or appropriate scripting languages, and statistical data analysis tools such as R, MATLAB or Python is required.
The ideal applicant should have demonstrated the ability to work independently and creatively. (S)he should have excellent communications skills and be able to articulate clearly the scientific and technical needs, set clear goals and work within an interdisciplinary setting, communicating with other partners within the Human Cell Atlas project.
Please apply online through www.embl.org/jobs <http://www.embl.org/jobs>
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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, as well as financial support for relocation and installation, including your family.
Please note that appointments on fixed term contracts can be renewed, depending on circumstances at the time of the review.
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