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Field
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
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of precision fermentation or cell culture - Affinity towards technical tasks and bioprocess control - Advantageous: Experience in mathematic modeling, programming, CAD, microfluidic or bioreactor systems
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the project “Modeling Great Ape Signaling Behavior” under the auspices of the Collaborative Research Center “Common Ground” (CRC1718), which is funded by the German Research Foundation (DFG), at the University
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and the effects of disordered correlated microstructures on diffusion; iii) development of energy-based models and numerical simulations of hyperuniform assemblies; iv) development and application
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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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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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modelling • A high fascination for technical/scientific problems of theoretical and numerical research • Experience in the field of numerical time-accurate flow simulation, preferably turbomachinery • Ability
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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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theories and numerical methods, carrying out and analysing field and remote sensing observations and conducting and analysing numerical model simulations. The PhD position is funded by the German Research