Mots-Clés

bioinformatics, computational biology, statistics, machine learning, optimisation, data mining, data science

Description

We seek a highly motivated bioinformatician or biostatistician who is well versed in the analysis of biological data and bioscientific programming for a project on the study of neurodegenerative disorders. The candidate should have experience in the analysis of large-scale biomedical data (omics, clinical or neuroimaging data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of Parkinson’s disease datasets, focusing on omics, neuroimaging and clinical data. This will include implementing and applying software analysis pipelines and jointly interpreting of disease-related data together with experimental and clinical collaborators. The project will use multiple layers of new biological high-throughput data from different patient subgroups and healthy controls. With the help of statistics, machine learning and pathway- and network- and analyses, the goal is to improve the mechanistic understanding of disease-associated alterations in Parkinson’s disease.

We offer:

• A fully funded position with a highly competitive salary.
• An opportunity to join the Luxembourg Centre of Systems Biomedicine with an international and interdisciplinary ethos.
• Working in a scientifically stimulating, innovative, dynamic, well- equipped, and international surrounding.
• Opportunity to work closely with international academic partners.
• State-of-the-art research facilities and computational equipment

Your Profile:

• The candidate will have a PhD or equivalent degree in bioinformatics, biostatistics, machine learning, computational biology or related subject areas
• Prior experience in large-scale data processing and statistics / machine learning is required
• A track record of previous publications in bioinformatics analysis of large-scale biological data (e.g. omics, clinical, structural bioinformatics, neuroimaging data) should be outlined in the CV
• Demonstrated skills and knowledge in next-generation sequencing data analysis, biostatistics, machine learning, pathway and network analysis are highly advantageous
• The candidate should have a cross-disciplinary aptitude, strong organizational and interpersonal skills, and a keen interest in collaborative biomedical research
• Fluency in oral and written English