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within a Research Infrastructure? No Offer Description How we measure and define performance shapes the systems we build. Current Natural Language Processing (NLP) benchmarks often prioritize leaderboard
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that you apply early as the advert may be removed before the deadline. This PhD project aims to develop a virtual tabletting laboratory by creating computational models that capture the multiscale mechanics
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geochemical field data and backed by numerical modelling of the lithium system. The focus is on small-scale volcano-sedimentary and closed basin sedimentary systems in Chile and the USA with existing geological
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This PhD studentship, based at the University of Exeter's Penryn Campus (Cornwall), offers an opportunity to conduct research at the intersection of wind energy, power systems, and advanced control
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develop physics‑informed neural surrogates to support real‑time digital‑twin CFD for offshore wind turbines. Key objectives include: Designing PINN and operator‑learning models that enforce incompressible
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into actively morphing and efficient structures? Applications are invited for a 3.5-year UK PhD studentship for the project "Design, Experimental Investigation, Aeroelasticity Modelling and Control of an Aircraft
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applications in aerospace, rail, automotive and battery technologies. However, current PFA systems suffer from brittleness, moisture‑related defects and narrow processing windows, limiting their wider adoption
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these internal conditions vary across space and time. Depending on the student’s interests and skills, the project may involve a combination of: • Experimental studies using model plant species to examine root
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, chemotherapy, viral infections and diabetes mellitus. Looking at clinical studies, the current research process is to propose a model, conduct animal tests and then evaluate whether the original model is
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remove the influence of specific data from trained models becomes critical. Yet, despite the EU’s General Data Protection Regulation (GDPR) enshrining a “right to be forgotten”, current federated learning