PhD Positions, Region Hovedstaden Psykiatri, Denmark (2017)

Institute of Biological Psychiatry, Copenhagen University, and DTU compute that are building on an international and highly competitive research environment, are now seeking a talented and highly motivated PhD candidates with interest in statistical modelling and genetics for a three year PhD Scholarship supported by the Lundbeck foundation.

Project
With a vision of creating a medical imaging tool to identify individuals at risk of developing disease, the PhD projects aim is to identify anthropometrical biomarkers, i.e. facial traits, associated with psychiatric disorders. This shall be accomplished by using 3D-imaging and analysis techniques to identify facial traits associated risk of sever psychiatric disorders and determination of the genetic basis of facial variation in both healthy and diseased individuals.
The project will based on A) existing material of >1500 unrelated individuals and B) a family cohort of >300 families with 3D images captured using a canfield Vectra M3 system and whole genome analysis using Illumina HumanOmniExpres arrays. C) New collection of individuals using both stationary and portable 3D imaging tools.
The project will involve analyzing 3D images and genetic data using different analysis and statistical tools. The research will build on a large state-of-the-art toolbox for facial analysis developed at DTU Compute. The candidate will primarily work at Rigshospitalet-Glostrup, however obligatory exchange with DTU compute and Copenhagen University is expected. The PhD candidate will work with the Research Leader and supervisor Thomas F Hansen and formally report to the Director of the Insti-tute, Professor Thomas Werge. Co-supervisors are Ass. Prof Rasmus R. Paulsen and Professor Haja Kadarmideen.

Qualifications
An ideal candidate shall have a background in genetics, bioinformatics or computer science and be experienced with pro-gramming tools and languages such as BASH, R, Python, C++ or MATLAB. Working experience with GWA analysis using programs like PLINK, GCTA, or IMPUTE2 or experience with image analysis in C++ or Python is considered an advantage.
A good candidate should be able to acquire the skills for handling large and complex datasets, do multivariate analysis and to handle different data resources like 1000G, Hapmap, GO, KEGG, and REACTOME.
The preferred candidate should have a strong drive, self-motivated and creative. An open-minded and analytical personality, with the ability to work independently and in structured groups is considered strong assets.

Further details:
http://www.nature.com