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dynamics or climate dynamics, basic shell scripting, and python/Matlab/R or similar languages. Experience with “traditional” climate modelling, data-driven climate modelling, and working with large ensembles
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analysis is a requirement Experience in using relevant software to perform complex tasks, e.g. R, ArcGIS, and Python is a requirement Experience in the mapping and modelling of ecosystem services is an
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background in statistics is required, as well as experience in atmospheric dynamics or climate dynamics, basic shell scripting, and python/Matlab/R or similar languages. Experience with “traditional” climate
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distribution modelling Experience with spatial analysis and mapping tools (e.g., QGIS, ArcGIS, or spatial packages in R/Python) Interest or experience in applying AI or machine learning methods to ecological
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projects The ability to carry out and publish high-quality research Knowledge on ROS, Python, C++ and Matlab The following qualifications will be considered an advantage when applicants are ranked: Practical
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presentation skills in English. Solid programming skills in Python, MATLAB, or Julia and knowledge of standard optimization tools (such as CasADi or Gurobi). Strong skills in mathematics, excellent capacity
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background in programming (Matlab, python, …) familiar with Multi-body dynamic tools good knowledge of statistics familiar with signal processing, operational modal analysis Personal characteristics Strong
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numerical modelling skills (e.g. Python, MATLAB, CFD codes) Personal characteristics Flexible and dependable Collaborative and independent Innovative and open minded Strong analytical skills Emphasis will be
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) Documented record of advanced quantitative methods skills in R and Python, specifically Experience with GIS and spatial data analysis Experience with natural language processing or text-as-data approaches
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skills in Matlab and/or Python are required. These should be documented, for example through a GitHub profile or similar. Familiarity with numerical methods for solving Maxwell’s equations, particularly in