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Field
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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to have experience with: Phase equilibrium calculation algorithms and their integration into CO2 capture simulation Thermodynamic modeling of phase equilibrium and thermophysical properties related to CO2
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Employment 1.0 FTE Gross monthly salary € 3,378 - € 5,331 Required background PhD Organizational unit Faculty of Science Application deadline 13 July 2025 Apply now Are you interested in a project
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control protocols, and hybrid quantum-classical algorithms. o Modeling and benchmarking atomic qubit architectures. · Collaboration: Work closely with experimentalists to design and optimize protocols
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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research in neuro-symbolic AI, with a focus on using generative AI and prompt engineering as a method to engineer knowledge graphs one can trust. This includes the design of algorithms and architectures, but