Furthermore, prior experience with high-throughput omics data processing, familiarity with advanced statistical modelling frameworks (including social epidemiology applications and longitudinal analysis), and a proven track record of peer-reviewed scientific publications in epidemiology or public health are highly desirable. The successful candidate will analyse population data to examine how social factors are associated with early biological changes, functional decline, and accelerated aging. * Statistical modelling and data analysis of large-scale, longitudinal population-based cohort studies (e.g., the IDEFICS/I. Family cohort and the NAKO Health Study). * Master's degree (or equivalent) in Biostatistics, Epidemiology, Public Health, Bioinformatics, Data Science, Statistics, Computer Science, Medical/Biological Sciences, or a related quantitative or biomedical discipline.
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