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coupling, applications, and development of regional ocean models. Capitalising and contributing to this effort, this project will Investigate effective downscaling strategies for different regional ocean
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describing the effect of conditions on stability. Testing the model in standard stirred tank apparatus Refining the model to allow predictability between different types of apparatus. Defining an algorithm
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research on the development of new inference methods and algorithms for wide classes of stochastic models. However, research will be conducted in collaboration with biologically oriented researches allowing
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using electromagnetic induction (EMI), and ground penetrating radar (GPR) will be combined with soil sensors systems and UAVs at different scales. In particular, we will combine borehole and surface GPR
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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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and significant piece of information to the right point of computation (or actuation) at the correct moment in time. To address this challenge, you will focus on developing theoretical and algorithmic
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to develop Computer Vision algorithm and Interfaces for collaborations with Historians teams. Specifically, the research will focus on structured data with clear repeated patterns, such as characters in a text
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) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance
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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
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provide large and complex datasets. By applying advanced pattern recognition and clustering algorithms, the aim is to automatically detect coherent spatial domains. These domains represent regions with