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Field
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large environmental and ecological data sets, incl. empirical, remote-sensing and simulated model data knowledge in programming languages (UNIX, C, or similar) and script-based programming (R, Python
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Experience with, or strong motivation to learn, programming (Python) and advanced data analysis methods Excellent oral and written communication skills in English Ability to communicate results clearly in
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, geoinformatics, geoecology or related fields Programming skills (Julia, Python, MATLAB) and ability to work with time series and/or geospatial data Motivation to work at the interface of ML and environmental
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, Bioinformatics or related fields with an interest in biology or Biological sciences with a strong background in programming Expertise and experience in programming, Python or R Expertise to work in a Linux
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languages (e.g. Python) Very good command of English (oral and written) Additonal skills and experiences that are advantagous: Experience in ocean dynamics Experience in ocean and/or biogeochemical modelling
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pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with colleagues from various application areas (cross-disciplinary) publication and presentation of your scientific results
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informatics, or a related field - Strong programming skills in Python and experience with deep learning frameworks (PyTorch preferred) - Experience or strong interest in large language models, multimodal
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mathematics Proficient in programming (Python and C++) Fluent in written and spoken English; knowledge of German is an asset Strong analytical and independent working style Excellent teamwork and communication
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, magnetic fields) and programming skills (e.g. Python) are desirable. Remuneration is based on the TV-L (EG 13, 26 h/week). The contract is limited to 3 years, which is the project duration, set by the German
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, ecology, mathematics, or a related field Strong programming skills, preferably in Python (experience with scientific computing, ML frameworks, high performance computing and geospatial data are highly