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such as landslide movement style, runout, and how landslide hazards evolve over time. This Ph.D. project will leverage the analysis of new time-series data from cloud-based satellite image archives
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with, cloud computing and virtualisation technologies Familiarity and hands-on experience with machine learning techniques desirable Desirable to have work experience (through internships or similar) in
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(NOₓ), and contrail-induced cirrus cloud formation. Aviation currently accounts for approximately 3.5% of total anthropogenic radiative forcing, with non-CO2 effects responsible for around two-thirds
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significantly reduce the amount of vibration data to be stored on edge devices or sent to the clouds. Hence, this project's results will have a high impact on reducing the hardware installation and operation
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cloud-computing (Azure, Google cloud platform, AWS) are considered beneficial but not essential. Funding This is a self-funded opportunity. Diversity and Inclusion at Cranfield We are committed