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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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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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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 CNC machining delivers high precision but is costly, rigid, and limited in adaptability. Robotic
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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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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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machine learning, Reliability Engineering & System Safety, Volume 264, Part A, December 2025, 111368 L. Deng, C. Shi, H. Li, M. Wan, F. Ren, Y. Hou, et al. Prediction of energy mass loss rate for biodiesel
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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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, Jose Landivar-Scott, Nick Duffield, Kevin Nowka, Jinha Jung, Anjin Chang, Kiju Lee, Lei Zhao, Mahendra Bhandari, Unmanned aerial system and machine learning driven Digital-Twin framework for in-season