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) of fiber reinforced composites. Experience with the finite element method and programming experience in scripting languages like Python or Matlab. The position on structural health monitoring requires
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to organic synthesis and properties of organic molecules, is an advantage. good knowledge of data curation and plotting, electronic laboratory notebooks, Python or similar, are merits. excellent grades from
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involving robotics, computer vision, SLAM, mobile sensing, sensor fusion, or related areas will be valued. Criterion a3. Technical and programming skills (30%). Strong proficiency in Python or C++, and
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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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Python and/or Julia using LINUX-based OS. Communication of findings through journal publications, conference presentations and project reports. Candidate Requirements Applicants must satisfy the criteria
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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 | 12 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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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