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potentially involving techno-economic analysis and AI-driven models for optimizing design and operation. Activities within project management and co-supervision of graduate students are also foreseen
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properties. In this project, we will apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and
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be to optimize existing technology and adapt the process for the needs of the partners working on the innovative recovery of lithium and other valuable metals. Please observe: Since the department is
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remanufactured. The established process is optimized considering the chemical process solutions during electrode washing. Finally, the process is evaluated regarding potential for scaling to larger battery formats
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sheet fluorescence microscopy Image analysis workflows Immunofluorescence optimization and protocol development About the Position This is a full-time, fixed-term position for two years, in accordance
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the Department of Energy Sciences. At the division, we conduct research in various fields, including research in design and optimization of turbomachinery, reactive fluid dynamics, multi-phase and turbulent flows
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expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security
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of their technological, nutritional, or sensory properties, is meritorious. What you will do Conduct research within the FAVOUR project with a specific focus on optimizing the flavor, functionality, and nutritional value
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, aiming to create automated and AI-assisted microscopy workflows for large-scale human cell imaging and modeling. You will be responsible for developing and optimizing wet-lab and imaging protocols
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towards co-optimized solutions that maximize full-duplex MIMO performance. As a postdoctoral researcher, you will also play a central role in driving the future research agenda, supervising PhD students