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multidisciplinary research team within the School of Electrical, Electronic and Mechanical Engineering. SMG is known for its work in computational mechanics, fatigue, fracture, and advanced materials modeling
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embedded in the cross-disciplinary TRDA project Entangled Lewy. The project brings together Health Sciences, Engineering, Computer Science, Health Economics, Arts, Humanities and Social Sciences to develop
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Fields: Open across ten schools covering disciplines such as Engineering, Construction, Computer Science, Law, Business, Arts and Creative Industries, Architecture, Health, and Education Duration: Standard
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offering one funded PhD position investigating how LLMs represent and process linguistic knowledge, drawing on tools from computer science, linguistics, and neuroscience. The aim is to build new
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and Language Sciences, including our Speech and Language Sciences subject area. The postholder should have a PhD in a relevant discipline e.g. Speech and Language Sciences, Education, Psychology
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PhD Studentship: Dissecting the Molecular Architecture and Function of LMTK3 as a Therapeutic Target
and International tuition fees (approx. £18k per year). Candidates should have (or expect) a minimum BSc 2:1. MSc degree is advantageous. Qualification should be in Biochemistry, Biomedicine, Biology or
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the sub-sized specimen testing program. You’ll also have the opportunity to present your work at international conferences/workshops. Finally, as a part of your graduate experience, you’ll have the
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in radiation–matter interactions, computational modelling, and materials science, with a strong publication record (h-index 36, i10-index 69). Dr Francesco Fanicchia, Research Area Lead: Material
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Award Summary This is a 4-year studentship. Stipend of £18,873 (stipend may be uplifted subject to confirmation) and home fees are covered. Overview Interested in using your computational and
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co-supervised by both academic and industry experts, gaining valuable skills at the interface of hydrogeology, environmental engineering, and computational modeling. Research Objectives (1