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. Experience in conducting experimental work (experimental internship) 2. Basic understanding of magnetohydrodynamics 3. Knowledge of Python programming 4. Experience in signal and/or image processing _Personal
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Sylt Ost, Schleswig Holstein | Germany | 5 days ago
with quantitative data analysis using R, Python, MATLAB or comparable software; GIS experience is advantageous Very good command of English (approximately equivalent to CEFR level C1); knowledge
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)medical or nutritional sciences.Preferably you have experience with working with large datasets in statistical software packages such as R, Python or SPSS. Proficiency in English at C1 level or higher (CEFR
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with experience applying these models to practical applications for real-world challenges. Good programming skills, Python is mandatory, other languages (e.g., Java, C++) will be considered a plus to
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evidence of excellent study results. You have strong programming skills in C and at least one high-level language (e.g. Python). You have a solid understanding of wireless network stacks and/or experience
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, engineering, or a related quantitative field - Knowledge of statistics - Interest in machine learning - Experience with R and/or Python If you are interested in this project, please select ten Cate your group
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technology, consumer influence, data science, artificial intelligence, computational modeling, reasoning and knowledge representation, digital communications, UX. • Programming in Python, R, SQL; experience
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, HVDC systems and inverter-based resources. Experience with PSCAD, EMTP, RTDS, OPAL-RT, PowerFactory or MATLAB/Simulink. Programming experience in Python, Julia, MATLAB, C/C++ or related languages
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of programming, computer vision, machine learning, or related discipline. Experience with Python and relevant AI/ML libraries (such as PyTorch, TensorFlow). Demonstrated knowledge of multimodal data processing
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discipline. Essential Good programming skills, preferably in Python/C#. Experience with machine learning, deep learning, or experimental AI evaluation. Interest in secure distributed AI, federated learning