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develop processes for the purification of hydrogen obtained from diol-based carrier molecules and to evaluate possibilities for the use of electrochemical compression processes. In this PostDoc position you
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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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reduction for fast and transparent access to the underlying compressed data including data processing pipeline has to be enabled. About your role: Development and testing of an innovative and federated
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(Shannon entropy, compressibility, effective complexity and logical depth). The data basis for the analyses are two-fold: first, recent connectome data, i. e. large datasets of all synaptic connections in
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information theory (Shannon entropy, compressibility, effective complexity and logical depth). The data basis for the analyses are two-fold: first, recent connectome data, i. e. large datasets of all synaptic
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are global leaders in the research of mobile and optical communication networks and systems, as well as machine learning, computer vision, and digital video compression. For an ongoing research project, we
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Data Management concentrates on the methodology of acquisition, organization, compression, analysis, and visualization of georeferenced or geometric data on large scales. We put emphasis on methods
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tackle the complex challenges of our time. What you will do Experimental investigations in the field of failure analysis, such as: Light microscopy Scanning electron microscopy Tensile and compression
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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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for electrochemical cells and stacks FEM simulation to investigate seal compression Manufacture of seals on a CNC dispenser and subsequent characterization What you bring to the table Studies in the field of mechanical