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Field
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the framework of the URANUS project. Main activities : Develop and optimize AESEC experiments coupled with electrochemical systems operating in highly corrosive and/or high-temperature environments. Design
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leverages AI and cutting-edge infrastructure to optimize EV charging and energy systems. By integrating distributed energy resources, demand response, and storage, it aims to enhance grid flexibility and
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, and related materials. Research activities will include developing solution-based processing methods, optimizing powder mixing and dispersion, fabricating dense composites through thermal consolidation
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of advanced photonic integrated circuits (PICs) for optical switching and processing applications. Key Responsibilities: The selected candidate will be expected to: Design, simulate, and optimize integrated
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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multiple ship classes and shore power applications. The project seeks to deliver industry-relevant modelling toolkits that enable optimal design and operation of greener vessels, backed by real-world
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related to staff position within a Research Infrastructure? No Offer Description Activities: The fellow will develop and optimize biocompatible micro- and nanosystems for the controlled release of
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. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor devices or metrology. We offer We offer a fully funded
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(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor
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. You will design and optimize resilience mechanisms that estimate disruption risk and proactively reconfigure the transport network to enable autonomous recovery, self-healing, and graceful degradation