50 phd-studenship-in-computer-vision-and-machine-learning PhD positions at Cranfield University
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This PhD project aims to address one of the key challenges in the manufacturing industry, the increase in productivity by utilizing the equipment with its optimum performance and without any
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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regulation and access to nature. By integrating Earth observation, spatial AI, machine learning and socio-environmental datasets, the project will reveal where blue networks perform well across UK towns and
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biodiversity support, cooling, air quality regulation and access to nature. By integrating Earth observation, spatial AI, machine learning and socio-environmental datasets, the project will reveal where blue
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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the Connected Waters Leverhulme Doctoral Programme, which is funding up to 18 PhD studentships to conduct multidisciplinary research on freshwater ecosystems, across two universities, Cranfield and Roehampton
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, and flexible working arrangements ideal for computational and field-integrated PhD research. Methodology You will develop a process-based, spatially explicit population model for European amphibians
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Leverhulme Doctoral Programme, which is funding up to 18 PhD studentships to conduct multidisciplinary research on freshwater ecosystems, across two universities, Cranfield and Roehampton. The programme aims
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This fully-funded PhD studentship, sponsored by the EPSRC Doctoral Landscape Awards (DLA), Cranfield University and Spirent Communications, offers a bursary of £24,000 per annum, covering full
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits