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* Molecular dynamics simulations * High-field NMR spectroscopy Ideal for students excited by computational biophysics and structural biology, with strong career prospects in academia, biotech, and AI-driven
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Satellite and earth observation together with RE technology implementation and advanced artificial intelligence to quantify the effect of REs in the terrestrial carbon cycle. Essential criteria Applicants
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welcome applicants from diverse academic and professional backgrounds, including (but not limited to): * Film, Television, and Media Studies * Artificial Intelligence and Computer Science * Creative Arts
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. The proposed PhD is a partnership with Age NI. Age NI have led two important online programmes recently. The Good Vibrations programme was a men’s health programme aimed specifically at men aged 50 and over. It
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Pharmacist-Led Medicines Optimisation in Metabolic care: a Cross-System Implementation Science Study
Apply and key information This project is funded by: Department for the Economy (DfE) Summary This PhD programme will develop and evaluate AI-assisted, pharmacist-led clinical decision-support
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Development of smart clothing for physiological monitoring of children with heart conditions at home
technology into smart textiles, with design suitable for wear by small infants. Monitoring will track breathing, heart rate, arrythmias and oxygen levels to provide clinical feedback and parental reassurance
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of the core business of built assets (e.g. hospitals) in integration with the engineering systems to achieve Low Carbon and Net Zero targets. This will create a new concept for developing inDTs which combines
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research methodology is coupled CFD (Computational Fluid Dynamics) and FEM (Finite Element Method) modelling and simulations. This is the only methodology allowing simulations of fluid-structure interaction
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foundations (e.g. concrete piles). These dual purpose ‘energy piles’ offer a sustainable and scalable solution for harnessing geothermal energy from underground soils and rocks, whilst serving as structural
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intraoperative feedback on these risks. The project will combine biomedical engineering, signal processing, and clinical collaboration to design a non-invasive ultrasound monitoring system capable of quantifying