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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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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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creating robust, low cost, and real-time edge-AI algorithms capable of accurately classifying diverse marine species and debris under complex and dynamic underwater conditions. The demand for such a low-cost
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silver artefacts. Specifically, we will seek to understand what detail is being missed, using current assaying approaches. The project will showcase what insights, at different length scales, could be seen
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sanitation industries. Working with our established industry partners, you'll implement your innovations in real operational environments, seeing your research make tangible difference while building
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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advance the development of the Tool’s algorithms and functionality. As a key innovative component of D-Suite, this open-source tool will achieve wide industry visibility, and will be formally evaluated by
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
Thermography. This raw dataset is needed to be processed and annotated to train supervised and unsupervised AI models. The research will aim to develop deep learning algorithms for damage classification
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temporal patterns across different neurons in the neocortical circuit and use them for closed-loop brain stimulation. By examining how these spatiotemporal dynamics relate to behaviour, you will develop new
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machine learning. The position will involve working with different research groups in the Department of Computer Science at the University of Cambridge, UK. In this collaborative project, we will apply