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interactions, and mobility are collected). Machine Learning models will be trained to infer fatigue in real time, triggering adaptive prompts, such as suggesting micro-breaks. Expected outcomes include a sensing
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machine learning with spectral data to enable rapid, non-destructive detection of food adulteration and fraud. Machine learning combined with spectral data can play a vital role in combating food fraud by
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and machine learning techniques for the design of superconducting qubits, one of the leading qubit modalities used in today’s quantum computers. The optimal design of superconducting qubits is a highly
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. The successful candidate will join the Intelligent Systems Research Centre (ISRC) at Ulster University’s Magee campus, working with experienced researchers in machine learning and cognitive analytics
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- 2025 - Business Strategy and the Environment - Wiley Online Library Additive Manufacturing: A Comprehensive Review Big data, machine learning, and digital twin assisted additive manufacturing: A review
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al. (2024) Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities. Advanced Materials. doi: 10.1002/adma.202312825. [6] K. Lee et al., “Secure Machine Learning Hardware
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, climate control, and irrigation systems, poses critical challenges to its economic viability and environmental sustainability. This PhD project aims to design, develop, and optimise a climate-smart, net
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approaches often provide only limited insight into these effects. This project will use advanced computer simulation, informed by post-operative scans and patient movement data, to understand how variations in
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Summary Meeting future energy demands requires efficient, low-carbon systems capable of storing and releasing heat when needed. This PhD project aims to develop next-generation latent heat thermal
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collaboration between HCI researchers, healthcare professionals, and computer scientists will ensure robust outcomes, while ethical considerations such as privacy and responsible AI use remain central. In summary