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, compression, learning, and inference for classical and quantum data exchanged through classical and quantum networks. The objective of the PhD study is to explore and address research and design challenges
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flow regime ranging from steady laminar to unsteady turbulent configurations, there is also potential to extend the analysis to compressible flows and structural analysis. This research is highly
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functions as offered by libraries such as BLAS and LAPACK. The mapping is by no means unique, and different mappings differ in terms of time, space, and accuracy. When the sizes of the matrices are unknown
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change in ultrafast optics pushed, among others, by DESY, one of our research topics focuses on the development of ultra-short pulse Ytterbium lasers employing multi-pass cell post-compression. In close
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efficiency. However, their complex behavior under combined loading conditions—compression, bending, shear, and torsion—poses significant challenges for both design and long-term performance monitoring
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for new quantum computing algorithms. It will rely on statistical structure learning represented by knowledge graphs and efficient low-rank tensor compressions. We are looking for: A completed scientific
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transition matrices, and incorporation of the dynamic models into the system integrator. About you MSc/MEng or studying towards a PhD in astrodynamics, aeronautical engineering or related discipline. Knowledge
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information from correlation matrices Maintenance of the GitHub repository for the developed code Organizing and managing joint experiments with the collaboration groups You must have a two-year master's degree
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their geometry when subjected to tensile or compressive forces, leading to measurable resistance changes. By applying the gauge factor equation, this resistance change can be translated into strain. Despite
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different conditions using existing software (written in Fortran). Analysis of data using quantitative genetics tools (e.g., calculation and comparison of genetic and phenotypic covariance matrices