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The Chemical and Fuel Cycle Technologies Division is seeking a Postdoctoral Appointee to conduct research in chemical process engineering, separations, and process intensification for recovery
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the internal application process. Welcome to The Ohio State University's career site. We invite you to apply to positions of interest. In order to ensure your application is complete, you must complete
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the internal application process. Welcome to The Ohio State University's career site. We invite you to apply to positions of interest. In order to ensure your application is complete, you must complete
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. The research programs are integrated from long-term research to short-term applications in linkage with incubation and start-up ecosystems. About CBS: The Chemical & Biochemical Sciences Green Process
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pretreatment, particularly in media filtration process. The successful candidate will join a dynamic, collaborative, and interdisciplinary environment with a strong tradition of combining both fundamental and
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applications in linkage with incubation and start-up ecosystems. About CBS: The Chemical & Biochemical Sciences Green Process Engineering Department (CBS) is a component of the Mohammed VI Polytechnic University
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project supported by OCP Green Water. We seek candidates with demonstrated expertise in seawater desalination pretreatment, particularly in media filtration process. The successful candidate will join a
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 19 hours ago
multiple tunneling processes to concurrently explore complementary regions of the Pareto front and exchange selected Pareto local optima. Implementation and scalability analysis of parallel tunneling
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-based and UV-curable inks. Implement FEM simulations linking chemistry, processing, and performance. Integrate experimental data from the experimental PhD’s for model validation. Collaborate with
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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