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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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(mechanical, electromagnetic, and, possibly, fluid-dynamic) and multi-scale simulations for drone components using finite element tools (e.g., Abaqus, Comsol). · Supporting the development of an AI
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area of machine learning. This includes conducting literature surveys and establishing state-of-the-art; developing necessary experimental and simulation facilities where required; planning, executing
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; developing necessary experimental and simulation facilities where required; planning, executing, and analyzing experiments and simulations; conducting joint and independent research activities; contributing
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-connectivity Communication system modelling, performance analysis, and simulation. Optimization tools and machine learning techniques. Hands-on experience with software-defined radios (SDRs) and/or
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attitude Fascination with multiscale organisation of biological systems and the complexity of the brain Experience in spatiotemporal dynamics and corresponding simulations (including high-performance
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numerical optimization and simulation models. Proficiency in one of the major programming languages such as Python A collaborative team player with a desire to make a personal impact within our
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in a number of the following topics: Turbulence modeling with wave propagation simulations Modulations used in optical wireless communications Data Analysis and Management Implement and open-source
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photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning, and theoretical analysis using Leslie-Ericksen
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alternative CO2 concentration trajectories will result in altered ecosystem dynamics and resilience. O3: Evaluate the ecosystem resilience simulated by a vegetation demography model with emerging signals