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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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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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R and Python, specifically Experience with GIS and spatial data analysis Experience with natural language processing or text-as-data approaches Familiarity with large-scale survey data, conflict and
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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 questions Personal attributes: Strong
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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
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dynamics, numerical modeling, and scientific programming is required. The following areas of expertise are considered beneficial: Experience in scientific computations using Python, C, C++. Experience in
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relevant programming languages (e.g., Python, MATLAB, R) is a requirement. Familiarity with downscaling and bias correction of climate data (e.g., from CMIP/PMIP) is an advantage. Experience with
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., Python, R, bash). At least one publication in an international peer-reviewed journal of an end-to-end software developed by the candidate Documented experience with Nextflow or Snakemake. Documented
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in scientific coding and data analysis programming languages, such as Python or MATLAB, is a requirement. Experience with running snowpack and/or Earth System Models is a requirement. Experience in
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. The candidate should have relevant scientific publications, showcasing research interest and experience in the core areas of the position The candidate must be proficient in Python, C++ or Java and familiar with