PhD Student Epidemiology, Statistics, Genetics (m/f/d)

You will be in charge of a work package of the project. The work will involve analysis of large-scale population-based cohort/biobank data including immunological, proteomic, and transcriptomic biomarkers. Use of genetic epidemiological methods (e.g., polygenic scores, genetic correlation, colocalization), methods designed to strengthen causal inference (e.g., Mendelian randomization analysis), and multi-omics data (e.g., genomics, proteomics, transcriptomics) will be a key part of your work. In addition, you will write and publish papers, present at scientific conferences/meetings and attend training activities. There will be an opportunity to develop new research skills and projects. The development of a personal fellowship and grant application will be encouraged.

You should apply if you hold an academic degree (master’s degree or similar ) in epidemiology, statistics, genetics or related fields and are keen to develop your career in epidemiology, genetics, and data science research to advance our understanding of potential drug safety issues. Experience in analyzing population-based cohort/biobank data, especially studies involving immunological, proteomic, genetic, and other biomarkers and health outcomes, would be an advantage. Knowledge and experience of genetic analysis, e.g., Mendelian randomization, genetic correlation, colocalization, would be useful, as will be experience in using data from publicly available data sources/repositories such as the UK Biobank, deCODE, and eQTLGen. There will be opportunity to learn and further develop these skills within the post through support and expertise available in the group and the wider community of collaborators.

The ideal candidate should have:

• Experience in epidemiology and causal inference (Mendelian randomization)
• Proficiency in standard statistical methods (e.g., regression models)
• Experience with genome-wide association studies (GWAS) and GWAS meta-analysis
• Experience with additional omics data (e.g., DNA methylation, transcriptomics, proteomics, metabolomics)
• Proficiency in Linux/Unix operating systems
• Expertise in R or Python
• Good communication skills and good command of English (German is a plus)

• Support for further personal development and training (e.g. structured development of leadership competencies)
• An interesting and versatile project with challenging tasks
• Access to the latest computing infrastructure
• Training and work in a multidisciplinary and committed research team
• Please note that, while we can be flexible, some level of in-person work will be required; it will not be possible to undertake this position fully remotely

Applications of women are specifically invited. In the case of similar qualifications, competence, and specific achievements, women will be considered on preferential terms within the framework of the legal possibilities. Handicapped candidates with equivalent qualifications will be given preference.