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Field
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AI approaches have recently been used to detect Alzheimer’s disease from CFPs among those with established disease (in case-control studies), the use of such approaches to predict disease (i.e., in
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energy; thereby minimising farming’s environmental impact. AI machine learning offers a new expedient method of developing control systems for tasks that would be difficult to manage using classical
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recycle content crush alloys. The main objective of the project is to understand the deformation behaviour of the high recycle content crush alloys and the role of tramp elements in controlling the final
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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
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research. The network's goal is to advance infection control protocols, improve implant safety and reduce healthcare costs associated with IAIs. The role holder will develop and characterise novel
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-fast, low-energy optical interconnects in collaboration with Microsoft and two Finnish SMEs. The project focuses on development new optomechanical control methods for macroscopic quantum states of light
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control. Joining the leading researchers in the Centre for Engineering Research at the University of Hertfordshire, collaborating with our industrial partners and becoming a member of the vibrant doctoral
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-controlled structural colours that respond to stimuli. You will develop the materials, methods, and designs necessary to 3D-print the next generation of structural colour devices, integrating optically- and
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, dye-free colour images, humidity and chemical sensors, anti-glare coatings and optical filters. This project will develop additive manufacturing of devices with actively-controlled structural colours
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will work on the design, development, and testing of integrated systems that may include laser generation and stabilisation, RF control, PID systems, and embedded electronics. The work aims to improve