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that yield valid statistical conclusions (inference) on causal effects when using machine learning algorithms and big datasets. The project is part of the research environment Stat4Reg (www.stat4reg.se ), and
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for environmental epidemiology (Epi, survival, sf, gstat, mgcv) and causal inference (dagitty, MatchIt), as well as contributing to reproducible, scalable data pipelines. Machine learning integration: Exploring ML
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the study of plant-plant or plant-invertebrate interactions Experience in nematology or nematological methods Strong quantitative skills (e.g., generalized linear mixed models, permutational methods, Bayesian
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mixed models, permutational methods, Bayesian analyses, machine learning algorithms, structural equation modeling). A good practical knowledge of R Personal characteristics To complete a doctoral degree
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health. You will develop and apply cutting-edge machine-learning techniques to identify the most informative indicators of ecosystem change and use them to build dynamic Bayesian network (DBN) ecosystem
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resources like UK Biobank. Advanced Techniques: Applying causal inference frameworks, unsupervised learning for phenotype discovery, and population-specific Genome-Wide Association Studies (GWAS). Ethical
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of interest, which will be used to compute the volumetric part of eigenstrains. Second, the bicrystallography will permits from the atomic structure of the crystals to infer the deviatoric part of
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exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised
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staff position within a Research Infrastructure? No Offer Description Are you excited about causal inference, real-world data, and methodological innovation? Join us to explore how the integration
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inferred from seismic data. A robust methodology for predicting sediment properties from geophysical data could reduce the number of boreholes required before investment decisions, enabling faster and more