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computational biologist for Computational Analysis of Microbiome Data

Details

Date & time May 27
Location
Heidelberg, BW, Germany
Creator admin
Category job-position-vacancy
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Description

We are seeking a highly motivated computational biologist to join the research group of Peer Bork within the Structural and Computational Biology Unit as a postdoctoral fellow. This bioinformatics group works on a wide range of topics with a current focus on microbiome analysis in ecosystems like the human gut and the ocean. We are offering this position within the context of the newly established National Research Data Infrastructure consortium “NFDI4Microbiota”, which aims to support microbiology researchers across Germany by offering open data analysis and integration services as well as training and data standardisation.

Your role
Your role would be to develop and run approaches for multi-omic data integration and the analysis of large-scale microbiomics data (metagenomes, metatranscriptomes, metabolomes etc.) for biological discoveries. For the development of these approaches, data will mostly come from various internal and external human microbiome projects, but also from ocean and soil. Within the NFDI4Microbiota consortium, you will develop data integration services that will put researchers’ data in context using relevant public data sets and databases, and expand the biological questions they can answer through multi-omic integration. Throughout the project, workflows you set up with the help of software engineers will be made available to outside scientists through a web-portal. The work will comprise both methodological development and applied aspects, and also aims at biological discoveries. You will work with colleagues both from the Bork group and other members of the consortium, and therefore have the opportunity to help shape a new national infrastructure for microbiota analysis and collaborate with microbiologists across Germany.
 
You have
A PhD degree in Biology, Bioinformatics, Computer Science, or a related field
Proven biological background knowledge and bioinformatics skills related to the group’s research
Experience in data integration, statistical analysis and visualisation, e.g. in Python or R
Experience developing new strategies, workflows or tools for data analysis
Competence in processing large microbiomics and/or multi-omics datasets
Ability to work both independently and in a team
Willingness to learn new skills as projects require
Good communication, interpersonal and English language skills
 
You might also have
Familiarity with processing next-generation sequencing data and performing metagenomic analysis
Experience working with the Linux command line and compute clusters
Experimental experience in microbiology
Experience working in large consortiums
 
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 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 you 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 approximately 2000 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. Please note that appointments on fixed term contracts can be renewed, depending on circumstances at the time of the review.

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