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/or eating disorders, data analysis and statistical modeling. Prior experience working with Danish registers is an advantage. As a person, the ideal candidate will have good interpersonal skills, will
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data. Statistical analysis using R and/or Python. Reproducible computational workflows. Scientific writing and publication. Microbiome research and host-associated microbial communities. The ideal
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methods and their underlying assumptions and limitations. Demonstrated experience with statistical analysis of genomic and ecological data, including multivariate analyses, demographic inference, population
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statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability
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materials. Use molecular dynamics, lattice-dynamical methods and statistical sampling to investigate local disorder, phase stability, temperature-dependent behaviour and structure-property relations
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-Norm) is preferred. Experience with citizen science, co-creation, or other participatory/qualitative research methods is an advantage. Strong quantitative and statistical analysis skills (e.g. SPSS, R
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Computational Sciences or similar. You have strong expertise on analyses of biology-related large datasets. Expertise in single-cell and spatial data analysis, spatial statistics and annotation is an advantage
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; survey design and online experimental methods; quantitative data analysis, preferably including choice modelling, willingness-to-pay analysis, segmentation, multivariate statistics, or related methods
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and students with a background in a number of different disciplines, including biology, molecular biology, statistics, chemistry, and computer science. About the research project We are seeking a
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nitrogen dynamics, and climate change mitigation potentials in agroecosystems. You will be contributing specifically to the area of regional simulation using process-based models and advanced statistical