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to automate, accelerate and improve decision making. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory, and algorithmics. Your future tasks: You actively
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fields at a manageable computational cost. You will be exposed to surrogate (AI/ML) approaches to accelerate micro-to-macro links, as well as uncertainty quantification to account for variability in
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materials synthesis, advanced operando characterization, and lab scale testing. We use robotic, high-throughput methods, and data science to accelerate novel sustainable materials discovery. PhD Position
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the development of scalable software tools and pipelines, potentially leveraging GPU/FPGA accelerators. Our aim is to build next-generation molecular atlases for chronic diseases and to improve patient