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Field
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academia and industry. Uncover and quantify critical degradation mechanisms to inform the design of next-generation fusion materials. Translate sophisticated scientific data into impactful insights through
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. This research will use distributed field data collection (macroinvertebrates, sediment character and dynamics) and a complementary set of flume experiments to quantify these impacts and to systematically
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nutrition and exercise trials Excellent oral and written communication skills Ability to critically appraise scientific methodology and data Ability to work well individually and as a member of a larger
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further developing both the experimental and data analysis methods that are currently used within the research team. The student will learn how to use the MMI apparatus, gaining knowledge of, for example
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, working with legacy print, manuscript, and digital sources. You will apply and adapt digital methods (especially TEI XML), analyse provenance data, disambiguate historical agents, and contribute
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to inform the design of next-generation fusion materials. Translate sophisticated scientific data into impactful insights through clear communication to diverse audiences, including industry stakeholders and
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several processing units with variable memory, can be profiled to pool the resources. The analytical systems, developed on data collected by onboard sensors and software triggers, can assist the operating
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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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the interview in person at City Campus. For informal enquiries and further information, please contact Prof Angus Pryor apryor@glos.ac.uk
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour