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Field
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the research. They will conduct relevant systematic and/or meta-analytic reviews to describe the current literature and inform their research. They will undertake qualitative data collection and analysis and use
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marine sciences, biological oceanography, ecology, or computer sciences. Strong analytical, numerical and practical skills are essential. Experience in coding or applying quantitative methods in a
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. Robust evidence is needed to ensure the resilience of flood defences and maintain ecological health. For further information on the project, we will be hosting a ‘Prospective applicant webinar’ at 2:00pm
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inaccurate, and rain gauge networks, while reliable, are too sparse to capture highly localised storms. Reliable, high-resolution rainfall data is urgently needed to improve flood prediction, climate
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data processing and nanoscale optoelectronics. This research is part of a UKRI Future Leaders Fellowship investigating “the hidden mysteries of light at the atomic scale”. The student will have
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honours degree (or equivalent) in a relevant discipline such as biology, ecology, microbiology, plant sciences, bioinformatics, or data science. The project is particularly suitable for students with
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with first-class honours or equivalent). Strong analytical and experimental skills are desirable. The project's specifics will be determined in collaboration with the successful candidate, tailoring
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honours or equivalent). Strong analytical and experimental skills are desirable. The project's specifics will be determined in collaboration with the successful candidate, tailoring the research
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the life cycle of its products. Existing practices often overlook indirect (Scope 3) emissions and fail to integrate real-time data analytics or life cycle assessments for decision-making. Therefore
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the Ven Te Chow Hydrosystems Laboratory at UIUC, using 3D-printed riverbed models derived from field data. Finally, insights from experiments will be incorporated into cutting edge flood models to enable