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/
Michael.blum@imag.fr

mblum