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of microfluidic devices. Simulation for microfluidics. (CFD) High Performance Computing and/or GPU programming for this domain. Machine learning algorithms for this domain Clean energy solutions (e.g., microfluidic
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., based on the 1D or analytical model) Hybrid simulation approach (e.g., which combine CFD and 1D simulations) High Performance Computing and/or GPU programming for this domain Machine learning algorithms
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will need strong coding skills to design highly efficient algorithms. Solid knowledge in the areas of algorithmics, optimization problems, as well as experience with SAT/SMT solvers or machine learning
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remote sensing imagery (e.g. roof material and condition, wall materials, type of building) Generating results from these algorithms for areas requested by our industry partner Documenting and packaging
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world is continuously sensed and/or explicitly shared to provide a model of 6G network environment and its users – encouraging design of algorithms, protocols, and systems that “intelligently” fuse data
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analysis, randomized data structures, high-performance computing, and quantum algorithms. Beyond this research, we aim at supporting computational thinking and computational problem-solving in the Earth
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The Chair of Robotics Science and Systems Intelligence (RSI) is a member of the TUM Munich School of Robotics and Machine Intelligence (MSRM), directed by Prof. Sami Haddadin. The MSRM is a globally
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for quantum computers and develop methods and software tools dedicated to the design and realization of quantum algorithms/circuits. We see ourselves as an interface between the stakeholders building physical