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, www.uni.lu ), offering broad methodological training and transferable expertise. Your Responsibilities: Conduct research in the field of Multiscale Computational Methods for novel metamaterials as described
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variability using dedicated tools (e.g., Dakota) and analyse uncertainty propagation on structural responses. Document methods and results through reports, figures, conference/journal publications, and
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ML frameworks like PyTorch/JAX Genuine interest in MLFFs, simulation methods, and foundational ML research Desired skills: Experience with atomistic simulation codes: ASE, FHI-aims, VASP, CP2K
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
materials science or a related discipline • Some knowledge of the theory of materials and experience with computational methods in materials science • Some experience with machine-learning interatomic
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related discipline • Some knowledge of the theory of materials and experience with computational methods in materials science • Some experience with machine-learning interatomic potentials • Good
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, developing knowledge, methods, and transferable software for the simulation and comprehensive sustainability assessment of socio-economic systems. Its purpose is to foster sustainable eco-innovation, through