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: Framework Development: Design and implement a generative deep learning framework for cross-modal integration and analysis, resilient to distribution shifts. Correlation Discovery: Identify interpretable
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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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emerging types of national emergencies and evaluate their spatial and operational implications. This will include an analysis of UK population distributions, terrain, infrastructure access, and airspace
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compound known to elicit the umami taste in humans. However, a desire to lower the sodium content of foods coupled with adverse consumer perceptions of this compound has led to the search for alternatives
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-modal datasets. It will use advanced analytical models to generate evidence about new and existing inflammatory pathways and how these will impact the progression of dementia. The PhD (DPhil) programme
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-on experience on SARRP and in vivo imaging modalities (CT, IVIS, ultrasound) Expertise in diet-induced fibrosis and surgical tumour models Image analysis, IHC, flow-cytometry Industrial experience with Xstrahl
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enable cohesive analysis across modalities and length scales. This PhD project is a collaboration between Swansea University and Carl Zeiss, a global leader in microscopy solutions. Both partners
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have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge of interdisciplinary