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the project/work tasks: Advanced data management on large datasets to facilitate data for statistical analyses Design and plan analyses Make efficient analysis pipe-lines in the statistical software R
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datasets to facilitate data for statistical analyses Design and plan analyses Make efficient analysis pipe-lines in the statistical software R for automatic statiscal analyses of a large number of
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, and python/Matlab/R or similar languages. Experience with “traditional” climate modelling, data-driven climate modelling, and working with large ensembles of climate/weather model output are advantages
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well as experience in atmospheric dynamics or climate dynamics, basic shell scripting, and python/Matlab/R or similar languages. Experience with “traditional” climate modelling, data-driven climate modelling, and
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the master's degree has been awarded. The candidate must have good knowledge in atmospheric dynamics. Proficiency in scientific coding and data analysis (e.g., Python, MATLAB, R, C++, FORTRAN) is required
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cycle processes, dynamics of oxygen and nutrient cycles, is required. Expertise in scientific scripting, programming, and data analysis (e.g., Python, Matlab, R) is required. Knowledge of climate
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obtain binding data and high resolution crystal structures (Espeland LO, Georgiou C, Klein R, Bhukya H, Haug BE, Underhaug J, Mainkar PS, Brenk R. An Experimental Toolbox for Structure-Based Hit Discovery
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using Python, R, Matlab, Julia or similar is required. Knowledge of energy systems, energy system modelling or the European energy market will be an advantage. Understanding of atmospheric processes
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modelling. Applicants must have good skills in programming (e.g. Python, R, Java Script) Experience (for example a master project or internship) working with snow or the cryosphere is a requirement