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resource-efficiency requirements. This collaborative doctoral project brings together the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy
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the training of state-space models Collaborate closely with our internal partners at PGI-14 (Neuromorphic Hardware Nodes) and international academic and industry partners Publish research articles and regular
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to facilitate a rapid and efficient exchange among experimental and computational groups and Devise an approach in invertible predictive modelling that links semiconductor properties to the composition of lead
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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-22 eV or better, and powerfully test the Standard Model of particle physics. They further constrain CP-violating new physics at scales of 10-100 TeV, far beyond the reach of the LHC. The TUM and the
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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developing and using dedicated tools and processors Contribute to our sparse auto-differentiation libraries to accelerate the training of state-space models Collaborate closely with our internal partners
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to model and analyse the intrinsic complexities of these systems. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains