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Field
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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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24.02.2022, Wissenschaftliches Personal The Munich Quantum Valley aims at developing a full quantum computing stack, from the application level to the physical quantum hardware. Within
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the Research Group “Stochastic Algorithms and Nonparametric Statistics” in the project „SFB/Transregio 388“ SFB/Transregio 388 investigates the interplay between rough analysis and stochastic dynamics. Central
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computer science with very good results - Interest on topics around the area of distributed systems and data management - Basic knowledge in distributed systems and graph algorithms is desired - Hand-on experience
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be achieved, for example, by developing learning algorithms and bringing together different sensor systems in the vehicle and on the road. Where you put the focus - that is up to you. The concepts
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(at places all over the world) and present your work at top-notch conferences and journals in our domain. You should have completed your Master/Diploma studies with top grades in Computer Science, Artificial
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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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• Integrated sensing and communication: fundamental limits and algorithm design (1 PhD, Mari Kobayashi, mari.kobayashi@tum.de) • Optical fiber channel modeling, receiver processing, and coding (1PhD, Gerhard
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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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mobility, user demands, wireless system deployment) as well as the digital world (such as networking, memory, computational resources for different applications). Information about the physical and digital