The Biomedical Data Science group at the Luxembourg Centre for Systems Biomedicine (University of Luxembourg) develops and applies bioinformatics software for the analysis of disease-related biological data in order to identify diagnostic biomarker signatures, characterize potential molecular drug targets and screen drug-like molecules. Our applications focus on the integrated statistical investigation of large-scale molecular and clinical data for complex neurodegenerative diseases, primarily for Parkinsonâ€™s and Alzheimerâ€™s disease. In particular, we develop machine learning methods for the joint analysis of multiple types of omics data from patient and control subjects by exploiting prior knowledge from cellular pathways and molecular networks. Integrative analyses of these data sources have the potential to provide models with increased robustness for diagnostic specimen classification and patient subgroup stratification. To elucidate specific molecular disease mechanisms, we complement these statistical investigations by bioinformatics approaches for network causal reasoning analysis, cross-species comparisons, the study of gender-specific pathological alterations and aging-related biomolecular changes as disease-promoting factors.
Potential ideas for the project: “The Biomedical Data Science group aims to contribute to a better understanding of early biomolecular changes in the age-related disorders Parkinson’s and Alzheimer’s disease by applying new causal network analysis techniques to large-scale, disease-related omics datasets. Within this research scope, we are looking for a candidate interested in working on a project that integrates diverse molecular datasets from patient and control subjects, as well as in-vitro and in-vivo disease models, in order to identify early molecular network perturbations that drive disease processes.
The ideal candidate should have:
An academic degree in bioinformatics or computational biology
Prior experience in large-scale data processing and bioscientific programming is required
Demonstrated skills and knowledge in next-generation sequencing data analysis, biostatistics, machine learning, pathway and network analysis
A cross-disciplinary aptitude, strong organizational and interpersonal skills, and a keen interest in collaborative biomedical research
Fluency in oral and written English
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