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mapping, to rapidly generate linked property-microstructure-chemistry multi-modal datasets, to efficiently produce low‑to‑medium fidelity data streams, which can also be paired to high‑fidelity industrial
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overseas. Training can be provided in computational fluid dynamics, machine learning, and nonlinear dynamics. These skills are highly valued across a wide range of industries. Recent data reveals that Fluid
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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio
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‑bounded turbulence. This short, intensive 9‑month position offers an exciting opportunity to advance fundamental turbulence research using DNS/LES and data-driven optimisation techniques. In this role, you
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stakeholders and an analysis of TIA clinic data you will help to understand how stratification might assist in delivery of more appropriately targeted care. You will develop mixed-methods expertise and join Dr
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role in supporting data-driven, policy-relevant research examining energy markets and a range of associated social and environmental challenges. This role is ideal for candidates planning to apply to top
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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will
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and experimental data, the student will develop predictive models of strand displacement reactions in non-ideal settings, in collaboration with the other students within BIOHYBRITE. These areas include
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Project Link: Image guided intervention supported with mixed reality | Project Opportunities | PhD | University of Leeds Eligibility: UK and International Funding: School of Computer
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project will transform rain garden design by linking plant traits to hydrological and ecological outcomes, ensuring resilience under alternating drought and flood stress. Working with the Royal