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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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The SDU Adaptive Intelligence Lab (ADIN Lab) (https://adinlab.github.io/ ) located under the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA
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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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The Section of Biostatistics is looking for a postdoc to develop statistical methods for inference on causal effects in studies affected by non-random participation, particularly self-selection in
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or Computer Science; Human-computer Interaction, Spatial Cognition or related areas; Engineering, Applied Mathematics, Statistics or another Quantitatively Oriented Discipline. Application procedure Your complete
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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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research questions, and be able to think critically and develop your own scientific ideas. Previous experience with statistical analysis, programming (e.g., R or Python), machine learning, or genomic data