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Currently enrolled in Bioinformatics, Data Science, Computer Science, Computational Biology, Statistics, or a related field Interest in microbiome research, ecological data analysis, and machine learning Good
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analytical techniques and their application to clinical problems. We expect the applicant to have some experience in statistical data analysis and to be a highly-motivated, precise-working person with
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Multivariate statistical analysis of community and environmental datasets Spatial analysis and georeferencing of ecological data using GIS Development and implementation of species distribution models
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economics Experience in working with data sets and statistical software (ideally R; alternatively, Python). Experience in working with Natural Language Processing (NLP) and/or Large Language Models (LLM
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international journals with peer review Outstanding knowledge in statistical and quantitative analyses methods; preferable are also experiences with longitudinal studies and the analysis of longitudinal data
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networks Analyze and interpret multiome datasets (e.g., transcriptomics, epigenomics and related modalities) Apply advanced computational and statistical methods for integrative analysis of multi-layer
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skills Good knowledge in statistical analyses, preferably in R Experience in molecular lab work (barcoding, metabarcoding, data analysis, phylogeography) Experience in mass-spectrometer data analysis, e. g
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biodiversity patterns, utilizing both spatial and statistical expertise. At this stage of the subproject of the CRC the project aims to evaluate whether ecological patterns previously identified in the RESIST
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, biogeochemistry or comparable studies good English speaking and writing skills good knowledge in plant and/or soil ecology basic understanding in chemistry and lab work comprehensive knowledge in statistics and
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programming language such as C or Fortran Strong expertise in data analyses and statistics Experience in ecological or environmental modelling Documented ability to publish scientific papers in international