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                student fees. Exceptional candidates may be eligible for an International Fee Bursary. Project overview: The project aims to use state-of-the-art methods to derive detail about the provenance of precious 
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                PhD studentship: Defining the role of the pioneer factor FOXA1 in hormone-dependent cancer Supervisor: Professor Jason Carroll Course start date: 1st October 2026 Project details For further 
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                EAF steels The transition to EAF steelmaking creates challenges for the reliable production of thin rolled steel poducts where residual element accumulation may compromise processability and properties 
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                chromatin profiling methods along with CRISPR/Cas9-meduated cell line engineering and various animal models. You will study the effects of the activation or depletion of chromatin-modifying enzymes using 
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                of cancer, namely adenocarcinoma, which was driven by a transcriptional pathway that involves Androgen Receptor (AR) and a pioneer factor called FOXA1 that helps tether AR to the chromatin. Recent new 
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                metallographic approaches is challenging due to the fine scale of features and limitations of stereology. Similarly, crystallographic methods such including laboratory X-ray diffraction cannot readily distinguish 
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                to quantify the phase fractions and lattice parameter evolution, which in turn will allow quantification of the phase transformations taking place. This approach has advantages over other methods as it utilises 
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                building blocks to elicit properties far beyond simple averaging over the component materials involved, instead giving exciting opportunities for new functionalities that are not found in natural materials 
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                independent researcher. We welcome applications from students who wish to apply innovative statistical methods to real biomedical problems in order to deliver key insights into human health and disease. Three 
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                will be tasked with the development of new models for the early detection of CIN cancers, applying bleeding edge computational methods and machine learning approaches to improve detection and