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Field
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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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becoming increasingly critical, particularly for AI in resource-constrained environments or at the edge. To address this challenge, semiconductor manufacturers have introduced dedicated Neural Processing
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of the CDT in Process Industries: Net Zero . The successful PhD student will be co-supervised by academics from the Process Intensification Group at Newcastle University. Nestling has designed a
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collaboration with good oral and written communication skills. Previous research experience in machine learning, deep learning and/or computer vision is essential.
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In the UK, industries such as pharmaceuticals, food processing, and agriculture generate significant amounts of organic wastewater. Annually, the UK produces 2.4 billion cubic meters of wastewater
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the next generation of process and chemical engineers, and chemists, to develop the new processes, process technologies and green chemistries required for the process industries’ transition to Net Zero
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for Home UK students only. Overview The PINZ CDT will train the next generation of process and chemical engineers, and chemists, to develop the new processes, process technologies and green chemistries
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and advanced signal processing to detect cyber threats in real-time while minimizing energy consumption. Digital twins, as virtual replicas of physical systems, enable continuous monitoring and anomaly
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Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering Research Group) Aim: Develop a mathematical model for analysing reliability and efficiency of medical processes