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physical systems. You will explore how the dynamic behaviour of nanomagnetic devices can be used to realise these KAN functions directly in hardware. Working with a combination of modelling, machine learning
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as the Insigneo Institute theme co-director for Healthcare Data/AI and the N8 Centre of Excellence in Computationally Intensive Research theme lead for Machine Learning. About the School/Research Group
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to combat antimicrobial resistance, prevent tissue damage in infectious and auto-immune conditions and fight cancer. For informal enquiries about the project, please contact Dr Gillian Tomlinson
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developing a computational model that simulates blood flow for ICH patients. The research will exploit a powerful new approach — physics- informed neural networks (PINNs) — that combines machine learning with
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malignant precancerous lesions in the mouth. To facilitate the machine learning model building, the virtual oral tissue models will be developed based on knowledge derived from tissue-engineered constructs
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3D embryo images (published and those generated in the Strawbridge Lab). to quantify cell numbers and lineages. A semi-automated pipeline using deep-learning-based segmentation (Cellpose-SAM), machine
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protein-making machines, are now known to be dynamically regulated. Our recent findings reveal a distinct and dynamic pattern of rRNA modifications that appear specifically during the process of ectoderm
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of human condensin complexes (1,2)-key molecular machines responsible for organising and segregating chromosomes during cell division. Understanding IDR-mediated interactions is a major challenge due to
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, advanced statistical methods and the potential to develop pioneering reconstruction and calibration techniques involving machine learning. The PhD will prepare equally well for a career in industry and
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Experience in developing software to a high standard using a range of computer languages and tools, ideally for applications involving the modelling, simulation and analysis of the large, complex and dynamic