As part of this project, high-throughput experimental systems will be enhanced using Design of Experiment (DoE) approaches in order to efficiently explore the high-dimensional parameter space of the electrolysis processes. DoE is essential for data-efficient exploration and optimization of the process parameter space, as well as for adaptive, data-driven machine learning approaches to map the electrolysis process to a digital twin. In parallel, data workflows and system control interfaces (application programming interface, API) are being developed to automate both process monitoring as well as process control. * Excellent Master's degree and subsequent PhD in chemical engineering, computational engineering, computational mathematics, data sciences / analysis, system or process engineering, or related fields * Comprehensive knowledge of data science, data ...
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