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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 17 days ago
programming languages like R, and/or Python. You have excellent communication skills and a willingness to collaborate across disciplines. You fit to us: if you have strong scientific curiosity and motivation
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techniques is an advantage • Prior experience with Python and R programming is considered an asset • Highly motivated, team-oriented, and well-organized • Good English communication skills (spoken and
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or for multiobjective optimization problems. Implement the developed algorithms (e.g., in Python) and evaluate their practical performance on artificial and/or real-world data. Teach tutorials (in English) for
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, catalysis studies - in particular electrocatalysis and Python programming Experience within the following fields is highly beneficial: Transmission electron microscopy, Electrochemistry, Image analysis, Big
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Science, Transportation Research Part B, or conferences such as SIGKDD, ITSC, CIKM) Strong programming skills in Python Strong interest in scientific research with the goal of obtaining a doctoral degree
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engineering, structural dynamics, basic isolation, finite element modelling. Proficiency and/or interest in programming languages (e.g. MATLAB, Python, R) and software platforms such as OpenSees, ABAQUS, LS
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practical experience in at least one programming language (preferably Python) Ideally, some practical experience in material characterization methods Structured and analytical thinking as well as a systematic
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tools (e.g., Python programming) is an advantage. You enjoy working in an international team and have good communication skills. Proficiency in spoken and written English is required. For more information
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, computer science, or a related field with an overall grade of at least “gut” (or equivalent, e.g., cum laude) Expertise in quantum mechanics, experience with HPC, programming (e.g., Python, C / C++), and/or
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computer science, bioinformatics or related fields Solid understanding of machine and deep learning and relevant frameworks (e.g. Pytorch or Tensorflow, Keras, scikit-learn, OpenCV) Proficiency in Python, Linux and