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Field
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/d) in Energy Informatics. You are passionate about applying cutting-edge information technology to solve the energy and climate crisis and would like to work in a vibrant and international research
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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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existing technologies, right through to the tested prototype. The Data-based Methods team at Fraunhofer ENAS develops real-world applications using AI, machine learning and computer vision. The main focus is
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of the novel methods created within our research group. Your primary tasks will include: - Assisting in the research taking place in our group. - Collaborating with our researchers to translate new algorithms
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detectors (Partial) automation of detector characterization for more efficient analysis Algorithm development: Development of a correction method based on information field theory for atmospheric image
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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
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strategies, and cross-functional collaboration — shaping the future of quantum computing. We holistically address business-relevant challenges using innovative quantum computing algorithms and demonstrate
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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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an extensive safety analysis and calidation of perception algorithms in automotive. Through our work, we lay the foundation for a reliable digital future. What you will do Reliably detecting persons is crucial
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multi-parameter ion-beam tuning procedures (collaboration with Univ. of Vienna and HZDR) and developments of machine learning (ML)-algorithms for optimization of beam parameters and control of relevant