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bio-optical (fluorescence and backscatter) data from a 2025 research expedition to the Bellingshausen Sea, West Antarctica, coupled with data from laboratory analyses of algal pigments, particulate and
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, are crucial for modelling volcanic processes and are vital for understanding the transitions. Geophysical monitoring provides essential information to constrain these parameters and inform decision
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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interventions. These insights may ultimately lead to the identification of new targets for the development of therapies for ageing and age-related disease. For more information please contact: rebecca.c.taylor
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. Describe site connectivity along global flyways, using a global dataset of important areas for seabirds (i.e., Key Biodiversity Areas) and data from the STD. Investigate the drivers of flyway connectivity
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on the topic (2,4). Training and Development Training will maximise future employability in academia and industry: Programming and geospatial data analysis using Python/R. Machine/deep learning techniques
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spectroscopy, and gas chromatography. Advice on scientific communication, writing, and data analytics will be provided. Presentation of data at national and international conferences is encouraged and the PGR
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ability to evaluate fossil fuel CO2 (ffCO2) emissions is currently limited. ‘Bottom-up’ emissions estimates, based on inventory-style accounting and mobile tracking data, can differ significantly from each
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. translocations) affect the distribution, and thus impact, of such mutations within endangered populations is limited. Addressing these knowledge gaps is important for realising the potential of genomic data in
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spectroscopic methods suitable for large-scale sample screening and eventual field deployment. The project will also involve developing your skills in data science, including multivariate analysis, machine