We seek a highly motivated bioinformatician, biostatistician or machine learning scientist who is well versed in the statistical and machine learning analysis of large-scale biological data for a project on the study of neurodegenerative disorders. The candidate should have experience in the analysis of complex biomedical data (omics, clinical, digital biomarkers or neuroimaging data), using statistical methods and machine learning. The candidate will conduct integrative stratification analyses, focusing on omics and clinical data, and digital sensor or neuroimaging data for neurodegenerative diseases. This will include the review, set-up and application of software analysis pipelines, and the joint interpretation of disease-related data together with experimental and clinical collaborators. The project will use new biological high-throughput data from patients, healthy controls, as well as in-vitro and in-vivo disease models. With the help of statistics and machine learning analyses, the goal is to improve the prediction of key disease outcomes in neurodegenerative disorders such Parkinson's disease and Alzheimer'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
Skills/Qualifications:
- The candidate will have a PhD or equivalent degree in bioinformatics, machine learning, biostatistics 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
Selection process: Applications should contain the following documents (combined into one pdf document):
- A detailed Curriculum vitae
- A motivation letter, including a brief description of past research experience and future interests, as well as the earliest possible starting date
- Copies of degree certificates and transcripts
- Name and contact details of at least two referees
Contact and where to apply:
enrico.glaab (at) uni.lu
