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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the
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the potential to accelerate materials design and optimization. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and
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subsurface and internal temperature distributions. Semi-destructive approaches, such as embedding thermocouples by drilling holes, can provide internal data but often disrupt the process, alter the thermal
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architectures and distributed storage integration. Examining the physical arrangement, fire safety, redundancy, and maintenance requirements for embedded storage. Evaluating economic considerations, including
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Project summary: A transition to net zero is desirable overall, but its costs and benefits are unevenly distributed across time, space, and between individuals, and policy to implement it must be
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workspaces under positional restrictions. Develop smart control algorithms that will allow the robotics end-effectors to communicate with the central control system and coordinate tasks with other end
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, distributed ledgers) Desirable: Experience with generative AI (e.g. LLMs) Interest in Human-Computer Interaction Interest in privacy enhancing technologies (PETs) Other: Experience in presenting or preparing
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1 and 2 and NQCC Testbed programme, will tailor the developed benchmarking approaches to error-corrected as well as distributed quantum computers, addressing the need for scalable benchmarks
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platform. Training the development digital-twin using real-time data from hardware available Electrical power level studies with developed digital twin to identify visible solutions for distribution electric