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on beaches. During the project, you will be involved in: setting up hydrodynamic coastal flow fields using SWAN, SWASH, SCHISM or a comparable model; writing python code to advect virtual macroplastic items in
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processing, and user-friendly GUI-based analyses for clinical and physiological research. You will combine Python-based software development, biomedical signal processing, and FAIR data design, and contribute
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science research, including familiarity with methods and tools (e.g., statistical software such as R and Python) common in social science; A strong track record in collaborative research, including
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hydrodynamic coastal flow fields using SWAN, SWASH, SCHISM or a comparable model; writing python code to advect virtual macroplastic items in these flow fields using the Parcels-code.org framework; exploring
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Advanced proficiency in Python and C programming languages You should also have good interpersonal and communication skills and should be able to work in a multi-cultural environment, both independently and
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languages, for example Python, and general purpose deep learning frameworks, such as Tensorflow or PyTorch; The interest and ability to share knowledge with other ESA organisational units. You should also
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quantitative data analysis. You have strong programming skills (e.g. Python, MATLAB, R). You have an excellent command of spoken and written English. You have a strong publication record appropriate to career
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and digitizing archival data, strong knowledge of causal inference methods, good command of R and Python. Knowledge of machine learning methods is an asset. Strong command of English; command of either
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R or Python). Good-to-have: You have experience working with large-scale text or visual data, or datasets related to history or culture. You tackle complex data challenges with curiosity and are
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-have: You can independently and confidently analyze quantitative data and you can write reproducible code (for example, in R or Python). Good-to-have: You have worked with large-scale text data, natural