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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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, refining, separation, process optimization, and simulation. The postdoctoral researcher will be supervised by Drs. Junli Liu and Nathan Mosier. Interested candidates should submit an application containing a
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environment,-you would like to focus on research and practical work with optimal number of classes,-you want to gain knowledge and skills necessary to have impact on the world,-you want your answer
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Decision Intelligence for Supply Chain and Operations Optimization. The successful candidate will contribute to cutting-edge research at the intersection of Statistical Machine Learning and Generative
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and ground), and boasts expertise in controlling and deploying them in practice, as well as in designing coordination strategies for them. Our recent work on ML-based co-optimization demonstrates some
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and their by-products. o Monitor and control the migration of radionuclides and heavy metals throughout industrial processes. 2. Optimization of Industrial Processes: o Utilize nuclear
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models and generate alphas · Execution. Propose improvements or optimize existing strategies Evaluation. Backtest ideas using historical market data and large research clusters Education. Participate in a
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alphas · Execution. Propose improvements or optimize existing strategies Evaluation. Backtest ideas using historical market data and large research clusters Education. Participate in a comprehensive
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modelling framework that can serve as a digital twin of the manufacturing process allowing for a faster and more precise optimization trough virtual engineering.You will work within a research team comprising