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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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, Quantitative Genetics, Population or Statistical Genetics). Demonstrated experience in analytical and quantitative skills. Proficiency in programming and data analysis tools (e.g. Python, R, Fortran, Linux
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, sediment transport/deposition, landscape change); You enjoy working with large datasets and applying statistical analysis and modelling approaches; You use scripting/programming in your research (e.g. Python
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flows, or reinforcement learning-based design optimization. Strong programming skills in Python with experience in PyTorch, JAX, or equivalent deep learning frameworks. Ability to work independently
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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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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