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Field
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The project: The deployment of generative AI—particularly Large Language Models (LLMs) based on transformer architectures—in industrial settings poses several critical challenges. Ensuring reliable
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reliability and operational efficiency. Determining the optimal size and location of PSTs within a network is inherently complex due to the nonlinear and dynamic nature of power systems, necessitating the use
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design that is applicable to different types of energy harvesters. This project will explore how PMC can be optimised to maximise efficiency, reliability and scalability using novel circuit topologies
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decision support systems will increasingly define the future. While significant strides have been made in the development of autonomous systems, their reliability and safety remain key concerns, particularly
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” analysis should in principle be able to extract information beyond the limits of current techniques, its development is still in an early phase. There is a still need to develop reliable ways to robustly
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responsive extracorporeal and implantable artificial organs. To achieve this, it pioneers advanced high-tech building blocks that can be produced both reliably and affordably. You will work independently, and
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. Through choreographed interactions with movement experts, this project expects to generate machine learning strategies to understand how people and robots can reliably and fluently move together. Expected
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metrics during both standard operation (primarily governed by system reliability) and extreme events (primarily governed by robustness and restoration). This will be achieved by building on previous
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analyses for the intended application and product. This includes evaluating the system’s ability to perform reliably under environmental and technical limitations, and establishing redundancies, where
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work tasks The applications and communication scenarios in 6G will be challenging due to stringent requirements in terms of latency, reliability, privacy, and efficiency. Furthermore, 6G networks