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that process planning has a high potential for automated optimization. Building upon this, you will advance our optimization pipeline and evaluate different optimization algorithms/strategies. What you will do
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on multi-view sensor perspectives of the “First Mile” test field validation of the developed algorithms and execution of tests on the test bed “Erste Meile” What you bring to the table student in Computer or
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! What
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of the internship depends on the requirements of your university. The weekly working time is 39 hours (full-time). The Fraunhofer-Gesellschaft attaches great importance to the professional equality of women and men
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) simulations and offer time-saving benefits. We are looking for a dedicated and motivated student to assist us in implementing a novel Graph Neural Network based algorithm that can act as surrogate for FEM and
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. The Applied Machine Learning (AML) group is part of the Department for Artificial
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systems, including their reliability and resource efficiency during production and operation. To support our team, we are looking for a working student in the field of “Classical and Quantum Computing
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university preferably in the subject areas of computer vision, computer graphics, computer science, media informatics or creative technologies Good programming skills in C++, C# for support in algorithm
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Announcement for fields of study such as: Automation technology, computer science, cybernetics, mechanical engineering, mechatronics, control engineering, technical computer science, technology
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processing algorithms for coherent optical communication systems What you bring to the table You are a student of electrical engineering, communications engineering, computational engineering, or a related