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“Stability and Solvability in Deep Learning”. This project focuses on mathematically analyzing machine learning algorithms with a particular focus on questions of stability, computability, and robustness
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the next era of the quantum revolution. Together, we research, develop, and design innovative quantum algorithms that push the boundaries of what is possible- especially in the natural sciences such as
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-based tiles can be arranged and actuated to form tunable metapixels, enabling dynamic control of light at the nanoscale. This project will integrate algorithmic self-assembly and nanomechanical switching
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(https://www.ise.fraunhofer.de/en/research-projects/pvev.html ), we are working to optimize models for PV self-consumption estimation and, on that basis, to develop an algorithm for PV feed-in upscaling
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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. Local slopes of a perturbed wavefront (for example, after propagation through turbulence) can be sensed using such a filter and a camera. From the measured slopes, the well-established algorithms used in
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Performing numerical simulations for metasurfaces Development and optimisation of analytical and numerical models for metasurfaces Documentation of the algorithms developed and results What you contribute You
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-world applications Documentation of the algorithms developed and results What you contribute You have strong background in machine learning and deep learning techniques You are enrolled in a Master's
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(rhizotron facility) and field trials. In addition to field applications, novel inversion algorithms for ground-penetrating radar (GPR) and electromagnetic (EM) will be developed. These algorithms will enable
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programming technology-specific evaluation algorithms Publication of results by writing peer-reviewed journal articles, conference contributions and giving conference talks Your qualifications: Successfully