德国海德堡计算生物学/基因组学博后职位

Job Description

A Postdoctoral position in computational biology/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.   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. Our research group combines the excellence in genomics and genetics on the Genome Campus in the UK with molecular profiling techniques and statistical computing at EMBL Heidelberg. This post will be linked to the H2020 funded project “PanCanRisk”, seeking to tackle key computational challenges in modelling the genetic and molecular risk factors of human cancers. We aim to derive new computational approaches for interrogating large-scale multi-omics datasets, thereby combining evidence across molecular layers to dissect genetic and non-genetic risk factors for disease.   The fellow will be located in the Stegle group and collaborate with partners in the project to develop analytical strategies for tying together genetic associations with molecular traits such as gene expression levels, epigenetic marks and information on 3d chromatin interactions. We seek to build on methods such as linear mixed models and (multi-view) factor models, deriving joint models for molecular risk profiles. These methods will be used to fully exploit and harness the datasets that are being generated within large pan-cancer initiatives (PCAWG), panels from human induced pluripotent stem cells (HipSci, http://www.hipsci.org), and other large international efforts we are involved in.
 

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 work.

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 statistical aspects of genomics, including gene expression data 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 modeling. 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 H2020 project.