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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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the Research Group “Nonlinear Optimization and Inverse Problems” (Head: Prof. Dr. D. Hömberg) starting as soon as possible. The project is part of a BMBF project concerning industrial scale data preparation
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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 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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-electronic and quantum technologies. What you would be doing: Experimental Design and Execution: Plan, conduct and optimize advanced 4D STEM experiments at cryogenic temperatures. This includes working with
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aspects of the research project(s), including design, fabrication, characterization, and modeling of metamaterial fibers and textiles. Develop and optimize fabrication processes for fibers and textiles
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clocks and laser links between Earth and the international space station ISS. The concrete task is to optimize our strontium lattice clock (DOI: 10.1103/PhysRevA.98.053443), transfer it to the Geodetic
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will focus on building understanding of the interplay between the upstream process parameters and process performance to develop approaches and strategies to optimize efficiency and robustness and allow
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Memorial Sloan-Kettering Cancer Center | New York City, New York | United States | about 2 months ago
critical quality attributes Optimize design and process parameters to ensure efficient production of highly potent mRNA Evaluate multiple DNA-based platforms for optimal mRNA potency and process parameters