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statistical analyses in R. You possess excellent written and oral communication skills in English. You have previous experience with participating in research projects (e.g. data collection, statistical
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good knowledge of psycholinguistics and bilingualism in particular. You have a strong background in experiment programming and statistical analyses in R. You possess excellent written and oral
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systems. Proven programming skills in Python, R, or a comparable language. Interest in developing methodologies to assess localized climate hazards, exposure, and vulnerability as inputs to impact-based
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(or strong willingness to learn) in programming and data analysis (e.g. Python, MATLAB, R, Fortran, or similar). Curiosity and motivation to work on fire emissions, air quality, and climate questions. Ability
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depth, but you may look into them for a high-level impression of the style of research. H. Fokkema, R. de Heide and T. van Erven. Attribution-based Explanations that Provide Recourse Cannot be Robust
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quantitative skills (analyses of long-term data, modelling and statistical analyses, particularly quantitative genetics, ideally in the R environment). Is proficient in English and meets the university’s
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depth, but you may look into them for a high-level impression of the style of research. H. Fokkema, R. de Heide and T. van Erven. Attribution-based Explanations that Provide Recourse Cannot be Robust
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data, GIS, and environmental modeling. Familiarity with programming languages such as Python, Julia, R, C++, or MATLAB is considered a strong asset. Experience with fieldwork or working with soil
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of neuroimaging data, such as Quantitative susceptibility mapping (QSM), Magnetic Resonance Spectroscopy (MRS), or (resting state) network analysis. Data analysis and programming skills (Python, Matlab, R) and a
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to acquire missing skills during the project in your motivation letter. Skills that are considered a plus: Programming for data analysis (e.g., R, Python) Multivariate data analysis, chemometrics and/or