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A Master’s degree (or equivalent) in a relevant field, such as epidemiology, bioinformatics, medicine, biomedical sciences, or a related discipline
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A genuine interest in scientific research and quantitative analysis
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Strong proficiency in written and spoken English
- Statistical knowledge and experience with programming in R
- Experience with epidemiological research or large-scale observational studies, particularly involving omics and eye imaging data, including the application of machine learning methods for pattern recognition and predictive modeling is desirable but not a prerequisite
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Strong analytical skills, independent and critical thinking, and attention to detail
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Flexibility, strong interpersonal skills, and enjoyment of working in an interdisciplinary and international team
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Proficiency in written and spoken German
The PhD project will be supervised by Dr. Dr. Petra Larsen, scientific lead of the ophthalmic epidemiology team. The research is embedded in the large population-based Rhineland Study, a comprehensive long-term cohort investigating determinants of brain health. We are seeking an enthusiastic and ambitious early-career researcher with a strong interest in investigating multi-omic and eye imaging signatures in the context of age-related macular degeneration.
The Ophthalmic Epidemiology research team investigates deep phenotyping of retinal ageing, preclinical changes and disease, as well as the comprehensive assessment of risk factors driving the onset and progression of both preclinical and clinical disease. These include genetic, molecular and environmental factors.
The PhD thesis will focus on the relationship between ageing and age-related macular degeneration and encompass the full breadth of scientific work, including the development and implementation of study protocols, coordination and supervision of data collection, and data analysis and publication.
The project will leverage data from the Rhineland Study, a deeply-phenotyped, large-scale population-based cohort study. Available data include comprehensive multi-omics data, including (epi)genomics, transcriptomics, metabolomics, lipidomics, and proteomics; eye imaging data, advanced MRI data as well as extensive behavioral and clinical data, including cognitive and neurological assessments, cardiovascular assessments, medical history, and medication use. The study currently includes over 14,000 individuals, of whom more than 6,000 already have longitudinal data. Additional baseline and follow-up data collection is ongoing.
