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carriers within defects. The charge transport will be implemented stochastically to mimic nature. A significant focus of the project will be to apply machine learning techniques to optimize the model and
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Testing and Experimentation Facility (TEF) for the energy field. Specifically, it leverages AI and cutting-edge infrastructure to optimize EV charging and energy systems. By integrating distributed energy
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finite-element simulation and topological optimization of light guidance in HCFs, and numerical simulation of thermo- and fluid dynamics under fiber-drawing processes. Apart from the main tasks above, the
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is to generate embryos from natural and iPSC-derived gametes and transfer them into surrogate Southern White Rhinos. The postdoctoral researcher (m/f/d) will derive and optimize differentiation
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. · Implement a Python-based workflows for automated execution of numerical simulations for parametric studies and design optimization. · Supervision of the development and manufacturing of physical
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industrial and academic partners. The overall goal of the project is to optimize the design of water eletrolyzers for efficient green energy production. You will be conducting Computational Fluid Dynamic (CFD
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learning paradigms as well as interactive data- and model exploration with domain knowledge towards optimal performance in real-world generalization scenarios. AqQua is a large-scale collaborative research
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tapes and investigating mechanical performance, environmental ageing, and material optimization for sustainable applications. Ideal candidates have a background in materials science, composites
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density files exist Validation, adaption, optimization of existing software written in C++ and Fortran Preparation and update of a user manual for the developed software Generation of global high resolution
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potential and to optimize the chassis for protein production. The generated data will enable you to address research questions such as: Which traits are key for robustness, and can they be identified early in