Novel approaches to stratify deeply phenotyped patients cohorts using blood-based biomarkers

This project will use large multidimensional human datasets to identify biomarkers able to predict the course of human disease. Parkinson’s disease is the most common neurodegenerative movement disorder worldwide. A critical challenge is to understand why the clinical presentations of each patient affected by Parkinson’s disease varies. For this, we will take advantage of the huge Accelerating Medicines Partnership Parkinson initiative (AMP-PD) that has generated vast amounts of deep molecular profiles across multiple Parkinson’s cohorts, including repeated deep clinical observational data and whole blood RNA sequencing profiles. The objectives of this PhD program, funded for 3 years, will be to develop computational/statistical/mathematical approaches that can predict how each patient’s Parkinson’s disease will manifest through the identification of molecular biomarkers. The candidate will combine clinical data, genetic information and blood profiles of Parkinson patients to identify these predictive biomarkers.

Interested candidates please send your CV to [Email Address Removed] & [Email Address Removed]