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develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
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, primarily due to validation gaps in high-pressure boiling phenomena and the lack of high-fidelity experimental data. Building on recent advances in non-intrusive, high-resolution optical diagnostics
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or further information, please direct all queries to Dr. Lulu Xu at l.xu@lboro.ac.uk Funding information: As this is a China Scholarship Council (CSC) based scholarship this is only open to Chinese nationals
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to a myriad of new techniques and much sought-after skills demanded by industry, including the following: Design of experiments, data harvesting/analysis and interpretation of results to derive insight
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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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respond to intense rainfall and enhanced sediment transport. The successful candidate will develop advanced skills in geomorphology, hydrology, environmental data analysis, and climate resilience
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, primarily due to validation gaps in high-pressure boiling phenomena and the lack of high-fidelity experimental data. Building on recent advances in non-intrusive, high-resolution optical diagnostics
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minimum English language requirements. Further details are available on the International website . Funding information: Studentship type – UKRI through FLOOD-CDT The studentship is for 3.5 years and
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. Funding information: Studentship type – UKRI through FLOOD-CDT . The studentship is for 3.5 years and provides a tax-free stipend of £20,780 per annum plus tuition fees at the UK rate. Due to UKRI funding