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of the workflow. While the majority of the project is computer based, there is a small lab-based component in order to generate cell samples to be able to acquire the NMR data. Once proof of concept has been
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properties of representative sediment classes. · Evaluate methods for predicting sediment type and physical properties from geophysical data using machine learning. · Assess the reliability
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properties of representative sediment classes. · Evaluate methods for predicting sediment type and physical properties from geophysical data using machine learning. · Assess the reliability
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modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia. A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning
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? This PhD project offers a unique opportunity to apply machine learning to solve a critical engineering challenge within the railway industry. The Challenge: Rail grinding is a crucial maintenance activity
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will also be able to access £2,500 of Proficio funding for professional skills development and conference attendance. Person specification You are passionate to learn more about sustainable fisheries
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avenues may include linking plankton size patterns to krill dynamics, carbon export or nutritional quality, or developing tools for rapid ecosystem monitoring using machine-learning approaches
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and resistance, and single cell RNA sequencing to characterise the resistant phenotype Apply mathematical frameworks to learn the dynamics of resistance evolution Combine experimental results with
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. You will focus on machine learning, but will be involved in all areas. There are also spinout opportunities. For details: PhD information sheet The team have wide experience studying bumblebee behaviour
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category