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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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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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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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areas of nanoscience and nanotechnology. Job Title: PhD student - Inorganic Nanoparticles Group Research area or group: Inorganic Nanoparticles Group Description of Group/Project: The Inorganic
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-driven AI approaches, we strive to deliver the cost-optimized solutions for development of materials with ultrahigh-temperature stability, high specific strength and structure-function integration through
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tissue engineering, including endothelial cells, smooth muscle cells, and stem cells. Design/Test novel 3D systems: Design and optimize 3D systems to mimic the physiological environment of blood vessels
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expertise and training to its users. Core Responsibilities: 1) Ensure optimal performance and reliability of core facility equipment through proactive maintenance planning. 2) Coordinate full-cycle
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to design and characterize optimized superconducting microwave resonators tailored for hybrid quantum experiments. · Perform precision measurements in dilution refrigerators at ICN2 and/or MIT, probing
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500 people, including researchers, PhD students and staff personnel. In its area of specialization and category, the institute is consistently ranked among the highest-quality research institutions