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artificial intelligence to learn from data and make decisions. However, the real world is always changing. Brain signals vary over time, and machines wear out or behave differently as they age. As a result
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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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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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-aligned investment, integrating sustainability metrics within transparent machine learning models. “An AI-Driven Connected Health System to Support Movement and Wellbeing during Preconception, Pregnancy and
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Apply and key information This project is funded by: Department for the Economy (DfE) Summary This PhD project offers an exciting opportunity to develop next-generation biocomposites made from
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Apply and key information This project is funded by: Department for the Economy (DfE) Summary CNC machining delivers high precision but is costly, rigid, and limited in adaptability. Robotic
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medicine in GBM, potentially transforming preclinical testing across multiple cancer types. Important Information: Applications for more than one PhD studentship are welcome, however if you apply
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vision systems addressing an urgent industrial challenge with immediate, large-scale impact. The project will take existing knowledge in computer vision and deep learning and apply it directly to a
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nutrition, genetic, lifestyle and environmental data; Aim 2. Utilise AI and advanced machine learning approaches to identify novel gene-nutrient interactions to inform personalised nutrition solutions
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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