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Do you want to contribute to groundbreaking research in the intersection of battery systems and data-driven optimization? This is an exciting opportunity for a postdoctoral position at the Division
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computational imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely
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imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely connected
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implemented globally. The research includes optimization of the electrochemical processes for mercury removal as well as safe and efficient retrieval of the removed mercury and regeneration of electrodes
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for industrial application. You will develop materials and processes enabling this technique to be scaled up and implemented globally. The research includes optimization of the electrochemical processes
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engineering. The work involves simulations for quantum error correction and mid-circuit operations, and will require both low-level optimization skills (e.g., SIMD, GPU, FPGA) and an understanding of quantum
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one of the most critical challenges in modern energy systems, maintaining stability in an increasingly converter-dominated power network. The project will be carried out in close collaboration with
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-driven, machine learning approaches. The biomass data product will be validated by data from an international network of ground-truth forest sites (GEO-TREES, geo-trees.org). The developed algorithms thus
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propulsion concepts to the preliminary aerothermal design of novel turbomachinery components and heat-exchangers. We have a strong European network with major aircraft engines OEM’s and we collaborate with top
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experimentation to address one of the most critical challenges in modern energy systems, maintaining stability in an increasingly converter-dominated power network. The project will be carried out in close