PhD position in bioinformatics/statistical genetics at the University of
Grenoble (France)
Title: Large-scale statistical methods to study biological adaptation
with genome wide dataset
The candidate will be involved in a multidisciplinary research project
that concerns a team a mathematical and computational biology in
Grenoble and a team of human evolutionary genetics at the Institut
Pasteur in Paris. The PhD candidate will work in Grenoble, which is a
French university town located in a beautiful alpine environment.
Subject: Because of the explosion of large-scale biological data,
statistical research efforts are increasingly needed in modern biology.
The project concerns the development of statistical methods to study
human genetic adaptation. Humans experienced several changes of their
environment, which triggered rapid biological adaptation. The shift to
agriculture was a prominent modification of their environment. They
adopt sedentary lifestyles, resulting in increased population densities
and modifications of their pathogenic environment that lead to novel
selective pressures. However, the extent and rapidity of the genetic
adaptation to such novel environments remain largely unknown. Based on
genome wide data (exome sequencing) generated by the Institut Pasteur in
Paris, we will investigate the occurrence of rapid adaptation through
various evolutionary mechanisms.
The candidate will develop original statistical approaches to detect the
regions of the genomes that have been involved in genetic adaptation.
Statistical models will be based on machine learning approaches that are
particularly well suited to handle large-scale genomic data. Numerical
implementations of the proposed approaches will be compared based on
simulations that mimic evolutionary processes of biological adaptation.
Profile: The background of the candidate can be in statistics or
bioinformatics. Students from related disciplines, such as physics,
computer science, mathematics or computational biology are also welcome
to apply. Applicants with a genuine interest for interdisciplinary PhD
education will be preferred.
Applicants should send by email a CV and a recommendation letter from an
academic reference.
Contacts:
Michael Blum
http://membres-timc.imag.fr/Michael.Blum/
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