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the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven
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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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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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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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of bespoke probabilistic models and/or evolutionary simulations, robust knowledge of and an affinity towards mathematical, computational or probabilistic modeling are important. Further skills in modeling and
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measurements in a team of experts on and in the pyramids and creating digital object models with numerical simulations, for example, using Salvus software or similar. Publication of research results and
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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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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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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