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Field
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skills (Python preferred). Familiarity with ML/Data Science frameworks: PyTorch, JAX, Hydra, MLflow, Pandas (or similar). Additional qualifications Experience with project management, planning and
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, or Python. Active participation in international collaborations and publications in high-impact journals. Motivation and ability to contribute to proposal writing and the development of R&D projects funded by
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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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languages (e.g. FORTRAN, Python) is required. A strong background and experience in operating, analysing and visualising large datasets is desired. Track record of peer reviewed publications in high level
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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 advantage Knowledge
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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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) 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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Python, MATLAB, C++, Julia, or similar Furthermore, experience within collaborative code development and usage of version control tools like git mixed finite element methods iterative numerical methods