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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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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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quantitative data analysis using R and/or Python; experience with statistical modelling and the analysis of complex biological data; a strong interest in exposomics, multi-omics integration, and brain–body
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excellent communication skills; have experience and knowledge in modeling and optimization, preferably with Python; have experience and knowledge of techno-economic assessment of energy projects; be able
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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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and written. Further expertise that would be valuable: Python software development; Natural Language Processing. Above all, you are a quick learner, proactive and eager to make things work. Our offer
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quantitative methodological skills, for instance knowledge of (spatial) data analysis, hydrological modelling, AI-based methods Experience with programming languages (e.g. Python) Motivation to cooperate in a
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, Introduction to Python, making figures using GGplot2 and basic machine learning. These courses are offered to PhD candidates through the PhD Course Centre of the Graduate School of Life Sciences . In
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: Ability and motivation to conduct fieldwork in alpine environments, and willingness to obtain a UAV license. Interest in coding (e.g., Python, Matlab, R) and numerical modelling. Enthusiasms for working
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, hydrological modelling, AI-based methods; Experience with programming languages (e.g. Python); Motivation to cooperate in a diverse and multidisciplinary research team; Strong social and communication skills