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: Experience with one or more general-purpose programming languages, such as Python, and general-purpose deep learning frameworks. Experience with integrating multidisciplinary inputs. Demonstrated ability in
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large scale datasets; Programming skills in Python and/or R; familiar with reproducible coding and automated (geospatial) data analysis; Familiarity or interest to dive into environmental or soil science
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strong programming skills (e.g. Python, Java, or similar) and experience building APIs, data pipelines, or backend systems. Data integrator by nature – You are comfortable working with SQL/NoSQL databases
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computational methods (e.g., NLP, text-as-data, Python/R) is highly desirable. Interest in serious games, interactive tools, or participatory research methods is an advantage. A research-oriented attitude
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programming skills (C++ or Python) and experience with numerical modeling (for instance, Finite Element Analysis or Computational Fluid Dynamics); A strong interest in—and willingness to learn and perform
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multi-disciplinary project. You also possess: a successfully completed MSc degree (120 ECTS points) in Human Nutrition, Food Science, or a closely related field; strong quantitative skills (e.g. R, Python
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and proficiency in Python (preferred) and/or MATLAB for data analysis are required. Experience with the assessment of masonry structures under multiple loading hazards. Language: Excellent command
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research, reflected in publications or other research outputs. Strong programming skills in Python and experience with scientific computing environments. Experience in one or more of the following areas
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interest in learning, adaptation, and dynamical systems in physical contexts Experience with analytical and\or computational modeling. Proficiency in numerical methods and coding (Python, JAX, MATLAB
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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