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running data-driven or hybrid hydrological models Strong programming skills (ideally in Python and/or R) Experience in working with large datasets, ideally hydrological, meteorological or climate
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-learning models on distributed systems Strong programming skills in Python and familiarity with a modern ML stack (e.g., PyTorch, hydra, zarr, dask) and best practices MLOps Experience in handling and
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. Strong programming skills in Python and familiarity with a modern ML stack (e.g., PyTorch, hydra, zarr, dask) Experience in handling and processing large datasets or experience in high-performance
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related field with experience in water chemistry, silviculture, experimental field work and large-scale data analysis. You must have good statistical skills and programming experience (e.g., in R or Python
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programming (R or Python). Advantageous: geostatistics, digital soil mapping, remote sensing, GIS, big data or cloud tools. Proactive working style, strong communication skills, and excellent English. Relevant
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and erosion processes. Skills in GIS, spatial analysis of large scale data, modelling, remote sensing, and programming (R or Python) are desirable. You work independently, communicate effectively
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automation, data science, python. The ability to collaborate in a multidisciplinary research environment is essential. Personal initiative, ability to work systematically, reliability, responsibility, teamwork
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Programming skills (commonly C++, Python, or similar languages used in HEP frameworks), Monte Carlo simulation methods and data analysis techniques Interest in connecting ideas and people across disciplines
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Advanced programming skills in Python, with deep experience in libraries such as PyTorch, Pyomo, and the broader scientific stack (Xarray, Pandas). Knowledge of MATLAB is desired but not a requirement A
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AI Strong general interest in the intersection between technology and management Proficiency in all stages of quantitative data analyses (e.g., using Python or R): from data wrangling and feature