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Field
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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
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production-grade system that integrates Vision Transformers for visual deepfakes, advanced Natural Language Processing (NLP) models for phishing detection, and a dedicated Explainable AI (XAI) layer
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approaches to interpreting these large datasets, as well as computational models that capture low-dimensional structure that reflects the architecture of the neocortex. By working with researchers developing
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The project: The deployment of generative AI—particularly Large Language Models (LLMs) based on transformer architectures—in industrial settings poses several critical challenges. Ensuring reliable
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reduction (MAR) algorithms, AI-based segmentation, and automated 3D anatomical modelling, promise clearer, more reliable imaging. Integrated effectively into clinical workflows, these advances have the
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will be an advantage if candidates have an interest in optimisation, control and computing systems, model checking, mathematical logic and good programming skills, ideally in MATLAB, Python and/or C/C
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vehicles, data centers, etc.). These devices are mostly power electronic interfaced introducing new types of dynamic phenomena and the need for more detailed models, increasing complexity. In addition
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prostate cancer risk across diverse ethnic groups. This work aims to support more equitable risk stratification in cancer screening programmes. Using simulations based on multistate modelling framework
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an important role in the efficient integration and management of solar energy in modern power systems. The studentship project aims to develop a novel PV forecasting model based on physics-informed neural
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models. This theoretical project will facilitate close collaboration with experimental groups and enable benchmarking of theoretical predictions. The PhD researcher will be part of the Correlated Quantum