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. This PhD will design methods that enable robots to achieve more robust, accurate perception and perception-driven planning for complex processes. You will investigate solutions like multi-sensing fusion (e.g
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its first two years of science operations. However, new modelling efforts are needed to fully interpret these datasets. In this PhD project, the researcher will use atmospheric modelling techniques
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. Previous experience in computational modelling, air quality or psychology is desirable. The Research Project The overall aim of this PhD project is to understand how indoor air quality (IAQ) affects
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involve experimental optimisation, leveraging computational tools, statistical modelling, and emerging AI/ML applications to streamline and accelerate the workflow for complex mixtures and metabolomics
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. This issue may help explain challenges in delivering major UK projects like Hinkley Point C. This project will build on the initial model to identify bottlenecks in UK infrastructure deployment, relating
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use generative AI to establish a large reference microscopy images dataset of healthy oral mucosa. The project aims to develop AI models that replicate the appearance and structure of healthy oral
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provide powerful tools to improve the quality and efficiency of data-driven models. In parallel to the development of data-driven models for dynamical systems with geometric structures such as Hamiltonian
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the underlying drivers of exploitation. Q2. Who have we missed? Using results for Q1, we will build predictive models, using a variety of techniques for cross-validations, to identify exploitable species that were
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season properties (e.g. number, intensity) for lead times ranging from one to approximately six months in the latest generation of dynamical seasonal and decadal forecast models. Seasonal forecasts
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, could also be used to underpin numerical models, quantifying the potential contributions of focal species to dispersal and/or reforestation at local, regional, or global scales. For further information