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optimize large-scale distributed training frameworks (e.g., data parallelism, tensor parallelism, pipeline parallelism). Develop high-performance inference engines, improving latency, throughput, and memory
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dynamic, uncertain worlds. Multi-Objective & Black-Box Optimization: Real-world problems rarely have a single, simple objective. We research methods to navigate complex trade-offs (e.g., performance vs
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interface between ESM and the latest version of SIESTA, its validation, its benchmarking, and its exploitation in scientific applications. Work will also involve the optimization of SIESTA on the fastest and
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ESEM, a FIB system, and a complete suite of sample preparation tools. The role will involve the coordination of day-to-day operations of the unit, ensuring optimal equipment performance, user
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. Evaluate and implement digital tools and platforms that support research activities, data management, and collaboration. Process Optimization: Analyze current workflows across research and administrative
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this short-term project we will use active learning to accelerate the training of deep learning algorithm to optimize 2D material Van der Waals structure discovery. Main Tasks and responsibilities: The person
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methods to accelerate the discovery and optimization of novel materials, and actively develop large-scale materials models (AI for Science) to transform the R&D process through AI-driven paradigms. In
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multidisciplinary environment. PhD Research Focus The selected PhD student will contribute to the design, fabrication, and optimization of electrochemical devices, exploring novel electrode materials, miniaturization
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maintain laboratory equipment, implementing upgrades as needed. The candidate must possess the technical expertise to operate, troubleshoot, and ensure the optimal performance of all instruments through
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and responsibilities: Testing of the microscope's components to assure full functionality. Installation of the system. Adapting the control electronics and software for optimizing user-friendly