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, or machine learning/data science applied to environmental problems. Project background Successful participants could use coupled global (CMIP) simulations, design and set up new model experiments using CESM2
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novel methods at the intersection of advanced control, optimization, manufacturing science and machine learning, to create the next generation of sustainable automation solutions for modern manufacturing
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combine computer-aided molecular and process design to optimize molecules and processes simultaneously. To holistically evaluate the environmental impacts of chemicals and energy systems, we develop
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simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and develop sustainable technologies to address societal needs. Fluid injection or production induces changes in
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Automation , we are seeking a driven postdoctoral candidate to explore the intricacies of traffic control management in contemporary urban environments. The emergence of new transportation modes like car
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navigation control or deep learning optimization for sensor fusion. Highly motivated, self-driven, and shows excellent performance. You have first-rate oral and written English skills and an interest in