Machine learning to integrate genome, transcriptome and patient outcome in neurological disease

Type de poste
Niveau d'étude minimal
Dates
Durée du poste
Contrat renouvelable
Contrat non renouvelable
Date de prise de fonction
Date de fin de validité de l'annonce
Localisation
Nom de la structure d'accueil
Adresse

Universiteitsweg 100
3584CG Utrecht
Pays-Bas

Contacts
Dr. Kevin Kenna
Email du/des contacts
K.P.Kenna@umcutrecht.nl
Description

Project outline:

Work to decode the genetics of a devastating neurological disease. You will focus on the analysis of non-coding DNA variants and the implementation of novel machine learning algorithms to predict biological impact based on patient DNA sequence and multi-omic profiling of human postmortem tissue and stem cell models.

Additional background:

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that causes near total paralysis. There is no cure but extensive research has revealed a central role for genetics. A deeper understanding of ALS genetics has the potential to revolutionize treatment and enable predictive counseling for patients and their families. To achieve this, the ALS community initiated project MinE, a global consortium that has generated whole genome sequencing for >10,000 patients and healthy controls. The key challenge for this PhD is working to determine which of the hundreds of millions of DNA variants revealed by whole genome sequencing are important to disease.

Candidate:

We’re looking for an enthusiastic candidate eager to engage in challenging high impact research. You will have an interest in big data, problem solving and analytical thinking. This 4year PhD project is intended to be 70% computational work (30% wetlab), but 100% computational is possible. We welcome applicants from any analytical discipline including but not limited to bioinformatics, genetics, neuroscience, data science, mathematics, engineering etc. Teamwork and basic English are essential. You are not required to speak Dutch (all meetings are in English). Preference will be given to applicants with prior experience in genomics, NGS library preps, machine learning, biostatistics or programming.

Work Environment:

University Medical Center Utrecht (UMC Utrecht) is consistently ranked among the top 15 universities in Europe (1st in Netherlands) and is an internationally recognized centre of excellence for ALS research and healthcare. The project supervisors have led multiple high impact ALS gene discoveries (Nature Genetics x3, Neuron x2) and act as the coordinating centre for project MinE. You will be embedded within the UMCU Brain Centre and integrated into an extensive research programme that spans genetics, translational neuroscience, stem cell models, environmental risk factors, neuroimaging, electrophysiology, patient care and clinical trials. You will also engage
with extensive expertise in the Utrecht Bioinformatics Centre.

Techniques:

  • Statistical programming & data analytics (R, python)
  • Deep learning (Keras)
  • High-performance computing (SURFsara Grid, UMCU-HPC)
  • Human genetics (disease gene discovery, variant interpretation, ancestry)
  • Analysis of DNAseq & RNAseq (bulk/ single cell)
  • Wetlab: RNAseq (bulk/single cell), target validation
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