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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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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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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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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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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