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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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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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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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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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-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
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programming and software development. Familiarity with Python and statistical computing libraries, like PyTorch or JAX, etc., would be preferred. You are a motivational teacher, with an encouraging teaching
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environmental datasets; proficiency with Python, MATLAB, or similar scientific programming environments. Ability to work with large datasets, develop reproducible workflows, and apply modern data science tools
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of practical interest, ideally with a focus on Intelligent Energy Systems. This should be demonstrated by a relevant MSc thesis or publications. Familiarity with good software engineering practices and Python
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. Skilled in MATLAB and Python. Experience with C++ and GPU programming (CUDA) is an advantage. Ability to work in a team, communicate effectively, coordinate multidisciplinary collaborations, and manage
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-time programming (C++, Python) Knowledge of the following is a plus: Real-time communication systems (e.g. EtherCAT, CAN bus) Closed-loop control of robotic systems Experience with experimental human