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from multiple departments. The aim is to develop and apply multimodal 3D imaging and analysis frameworks to quantify tissue architecture and protein expression in paraffin embedded tissue samples
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develop and apply multimodal 3D imaging and analysis frameworks to quantify tissue architecture and protein expression in paraffin embedded tissue samples at scale. By integrating AI-drive image analysis
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Fellow (several posts) in the Department of Chemistry (Ref. 536698), to participate in a cutting-edge research programme on “Smart Power Systems for Autonomous Platforms,” focusing on next-generation
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to 3D transistor architectures and stacked, hybrid-bonded devices, it becomes important to study the initiation and propagation of defects due to thermal and electrical phenomena (e.g., dielectric
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optimization of large language models (LLMs) and related architectures for generative tasks, continuous learning, indexing or retrieval, support of retrieval augmented generation over many data points from long
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to 3D transistor architectures and stacked, hybrid-bonded devices, non-destructive, high-resolution 3D imaging is becoming a necessity. Unfortunately, current 3D X-ray nanoimaging methods
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to 3D transistor architectures and stacked, hybrid-bonded devices, it becomes important to study the initiation and propagation of defects due to thermal and electrical phenomena (e.g., dielectric
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vertically. The project combines urban studies, remote sensing, 3D modelling, environmental science, architecture, political science and participatory approaches to investigate the environmental, social and
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such as quantum and analog computational models. You will explore how compilers, runtimes, and AI-driven agents can co-optimize complex architectures, reasoning across conventional processors (CPUs/GPUs
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About the Role The School of Electronic Engineering and Computer Science at Queen Mary University of London seeks to recruit a Research Assistant (RA) or a Postdoctoral Research Associate (PDRA