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optimization of electrolysis from the cell to the stack requires automated monitoring, analysis, and control of the operating parameters and processes. As part of this project, high-throughput experimental
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Stellenanzeige teilen Are you passionate about understanding how artificial intelligence behaves and how it can be optimized to deliver meaningful impact in medicine? Join an interdisciplinary research environment
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the fabrication process Your Role: Develop & test acoustic metamaterial concepts for Megahertz frequencies Conduct numerical simulations of sound fields at the unit cell and device level Optimize geometries
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FDSOI devices for neuromorphic computing applications Develop and optimize semiconductor fabrication processes in a state-of-the-art cleanroom environment Perform electrical characterization and analyze
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of acaricides, and in vitro models to investigate the transmission of tick-borne pathogens. Job description: Independently conduct a research project focused on the development and optimization of artificial tick
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of energy materials Develop and optimize advanced X-ray optical concepts with emphasis on maximizing photon flux, transmission, stability, beam quality, and experimental flexibility Design next-generation
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and Jülich. Initially, the work will primarily take place in Aachen Scientific Exchange: The opportunity to attend national and international conferences Work-life balance: Optimal conditions for
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to provide scalable, faster-than-real-time capable methods and tools that enable the energy-optimal, cost-efficient and safe design and operation of future energy systems. Your Job This postdoctoral position
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University of Munich is required. Salary and benefits are in accordance with the German public sector scale, TV-L E13. - Your Profile 1. Strong mathematical background (e.g., linear algebra, optimization
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-goettingen.de). Your tasks In-depth structural and functional characterization of optimized Channelrhodopsin variants for basic research in neuroscience and future optogenetic therapies Recombinant protein