Job:Post-Doc Biostatistics/Bioinformatics for high-dimensional transcriptomic data
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Last seen 5.1 years ago

Position available for 36 months

Starting at 01 sept 2017

Location : Lille, France

Lab : INSERM U1011 Récepteurs nucléaires, Maladies Cardiovasculaires et Diabète

Contact :

Lab presentation:

Our laboratory has a long standing interest in studying metabolic diseases. The recent

advent of large scale Omics data sets has launched a new era in biological sciences. We aim at

developing appropriate bioinformatic tools which will provide manageable information to biologists.



The applicant will contribute to a reasearch program aiming at identifying markers/regulation networks involved in different Non-Alcoholic Steato Hepatitis (NASH) stages. This Research Program will benefit from data obtained from human cohorts.


The major scope is the statistical inference from transcriptomic data (micro-arrays) of this cohort, and their integration with other high-dimensional data publicly available or with bioinformatic cistromic models developped in the lab. Analyses will start with micro-arrays data and will be completed by clinical and metabolomic parameters for the cohort. Then the second part of this project aims at redefining a standard pipeline for transcriptomic analyses in the laboratory and at developping it in R.

Profile: PhD in Bioinformatics and/or Biostatistics

  • Experience in statistical analysis of micro-arrays data, and knowledge of omics technologies,

  • skills in multivariate methods and statistical learning in high dimension, dimension reduction methods (data modelisation, inferential statistics...),

  • knowledge and experience in R/Bioconductor,

  • Autonomy and team spirit,

  • Organizational skills, ability to provide synthetic and didactic presentation of scientific results are also required (interface with molecular biologists)

  • A previous experience in regulation network modelisation and high-dimensional data integration would be an asset.

bioinformatician/biostatistician position statistical inference high dimensionnal omics R Job • 840 views

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