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virtual environments Strong quantitative skills (R, Python, or Stata; experience with machine learning or advanced experimental methods is a plus) Familiarity with VR-related toolkits (e.g., Unity, Unreal
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quantitative comparison with reference data. You have prior programming experience in Python and have used version control. You are curious, self-motivated, and interested in using modeling and quantitative
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related field. Experience or a strong interest in tree-ring analysis, dendrochronology, or wood anatomy is expected, and familiarity with data analysis tools such as R or Python would be an advantage
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Python and a profound knowledge of state-of-the-art statistical methods for climate data analysis Working experience with climate (model) data, in particular with large climate model ensembles, and
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processes Contribution to field experiments on soybean diversity and crop traits Statistical analysis (e.g., in R or Python) and interpretation of complex datasets Presentation of results at international
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, such as model evaluation and data pipelines, and their applied forms, including training, fine-tuning, and deployment of models using frameworks such as PyTorch or TensorFlow. Solid proficiency in Python
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candidate has strong analytical skills and programming experience in Matlab, Python, C/C++, or equivalent, and is able and/or eager to develop and implement signal-processing algorithms in such a programming
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, reproducible data pipelines, descriptive analysis, and forecasting-related tasks. Prior experience in handling and analysing data using statistical software packages such as R, Python, and/or Matlab is desired
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communication skills, enthusiasm and scientific curiosity. Strong programming skills in Python. Experience with analysing biological data. High level of motivation for academic research work. Good organisation
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Python, C++ or similar Experience with data processing and model interpretation Ability to work independently and collaboratively in an international research environment Good written and spoken English