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 analysis, data management as well as machine learning * Experience with data-driven machine learning (SINDy, LASSO, SISSO packages)
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