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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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, 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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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
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proven by publications in this field Have a good understanding of NMR theory and computer simulations Understand the basics of NMR and EPR hardware Be able to optimize and maintain a home-built DNP system
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variability described in the data by means of uncertainty aware calibration and Bayesian estimation Include how experts currently operate and acquire feelings for the machine they are driving, also based on a
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machine learning to develop an automated design process of mechanical walking aids, analyse gait patterns and make biomechanical simulations embedded in the generative mechanism design process. In
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construction or related fields Knowledge and experience in data analysis and database management, as well as emerging technologies applied to the construction sector, such as BIM, scanning, machine learning