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sources. This project is at the forefront of leveraging recent advancements in deep learning, particularly in computer vision tasks such as object detection and semantic segmentation, to enhance geographic
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collaboration between computational researchers with excellent technical skills in AI and machine learning, and environmental researchers with strong knowledge of application domains including climate change
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Revolution Industrialisation Centre at the University of Nottingham and contribute to the Power Electronics, Machines and Drives Research Group (PEMC). Your research will focus on electrical machines, drives
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University Belfast is an excellent opportunity to gain practical experience and training. You will work alongside experienced colleagues, whilst learning on the job and appropriate objectives will be set
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will be to shortcut the current search process in classify crystallographic orientation. This will be built upon where machine learning algorithms will be developed to extract material elasticity
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built upon where machine learning algorithms will be developed to extract material elasticity information, phase and even composition all from a single set of laser ultrasound measurements. Environment
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subject to performance. Closing Date: Monday 03 June 2024 Reference: ENG171124 Over £50 million has been invested in the Power Electronics and Machines Centre (PEMC) at the University of Nottingham
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PhD Supervisor: Antonia Marcu Supervisory Team: Antonia Marcu, Jonathon Hare Project description: Deep Learning (DL) is a widely successful tool. However, there are many fundamental challenges left
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system. They will then assemble the necessary climate input files to run the code over several years. Specifically, they will have to run tagged tracer runs to determine a sensitivity matrix of local
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for raster-to-vector alignment and/or label correction in geographic datasets. Explore unsupervised learning and foundation models to reduce user intervention and improve data quality. Achieve measurable